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Activity Recognition with End-User Sensor Installation in the Home

by

Randy Joseph Rockinson

B.S. Computer Science

Georgia Institute of Technology, 2004

Submitted to the Program of Media Arts and Sciences, School of Architecture and Planning, in partial fulfillment of the requirement for the degree of

Master of Science in Media Arts and Sciences at the

MASSACHUSETTS INSTITUTE OF TECHNOLOGY February 2008

0 Massachusetts Institute of Technology, 2008. All Rights Reserved.

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/ Randy Rockinson

Program in Media Arts and Sciences January 18, 2008

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Certified by X I

~-Kent Larson Principle Research Scientist MIT Department of Architecture Thesis Supervisor

Accepted by

Prof. Deb Roy Chair, Program in Media Arts and Sciences

ROTCH

MASSACHUStTTS INSTITUTE OF TEOHNOLOGY

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Activity Recognition with End-User Sensor Installation in the Home

by

Randy Joseph Rockinson

Submitted to the Program of Media Arts and Sciences, School of Architecture and Planning, on January 18, 2008, in partial fulfillment of the requirement for the degree of

Master of Science in Media Arts and Sciences

Abstract

During the past several years, researchers have demonstrated that when new wireless sensors are placed in the home environment, data collected from them can be used by software to automatically infer context, such as the activities of daily living. This context-inference can then be exploited in novel applications for healthcare, communication, education, and entertainment. Prior work on automatic context-inference has cleared the way to a reduction in costs associated with manufacturing the sensor technologies and computing resources required by these systems. However, this prior work does not specifically address another major expense of wide-scale deployment of the proposed systems: the expense of expert installation of the sensor devices. To date, most of the context-detection algorithms proposed assume that an expert carefully installs the home

sensors and that an expert is involved in acquiring the necessary training examples.

End-user sensor installation may offer several advantages over professional sensor installations: 1.) It may greatly reduces the high cost of time required for an expert installation, especially if large numbers of sensors are required for an application, 2.) The process of installing the sensors may give the users a greater sense of control over the technology in their homes, and 3.) End-User Installations also may improve algorithmic performance by leveraging the end-user's domain expertise.

An end-user installation method is proposed using "stick on" wireless object usage sensors. The method is then evaluated employing two in-situ, exploratory user studies, where volunteers live in a home fitted with an audio-visual monitoring system. Each participant was given a phone-based tool to help him or her self-install the object usage sensors. They each lived with the sensors for over a week. They were also asked to provide some training data on their everyday activities using multiple methods. Data collected from the two studies is used to qualitatively compare the end-user installation with two professional installation methods. Based on the two exploratory experiments, design guidelines for user self-installation of home sensors are proposed.

Thesis Supervisor: Kent Larson

Title: Principal Research Scientist, MIT Department of Architecture

This work was funded by National Science Foundation Grant #0313065 and the MIT House n Consortium.

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Activity Recognition with End-User Sensor Installation in the Home

by

Randy Joseph Rockinson

The following ind viduals graci usly served as readers for this thesis:

Kent Larson Principle Research Scientist MIT Department of Architecture

Dr. Stephen Intille Research Scientist MIT Department of Architecture

Dr. Pattie Maes Associate Professor of Media Arts and Sciences MIT Media Laboratory Adviso

Reader

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Contents

Abstract 2

1. Introduction 11

1.1 Context-Aware Applications in the Home 11

1.2 Sensor Installations: Questions Facing Context-Aware Applications in the Home__ 13

1.3 Approach 14

2. Sensor Installations 16

2.1 Defining the Sensor Installation Process 16

2.1.1 Sensor Purchase 17

2.1.2 Sensor Placement 17

2.1.3 Sensor Labeling 18

2.1.4 Gathering Examples and Building Models of Activities 18

2.2 A Scenario 19

2.3 Two Approaches to the Sensor Installation Process 20

2.3.1 The Professional Sensor Category 20

2.3.2 The End-User Sensor Installation Category __22

2.4 Cost 22

2.4.1 Purpose-Built Context-Aware Enabled Houses 23

2.4.2 Professional Sensor Installations 24

2.4.3 End-User Sensor Installations 25

2.5 The Dynamic Nature of the Home 26

2.6 Understanding and Control 26

2.7 Adapting to the Home 27

2.7.1 End-User Sensor Installations: Advantages 28

2.7.2 End-User Sensor Installations: Challenges 31

2.8 Inferring Context: Teaching the System 32

2.8.1 Rule Specification 33

2.8.2 Models 34

2.8.3 Impact of an End-User Installation Approach 39

3. A Review of Prior Work 41

3.1 Domestic Context-Aware Applications 41

3.1.1 Understanding Behaviors 41

3.1.2 Cognitive Aids 42

3.1.3 Social Connectedness 42

3.2 Context-Aware Sensor Technologies 42

3.2.1 Complex Sensors 43

3.2.2 Wearable Sensors 43

3.2.3 Object Based Sensors 44

3.3 End-User Sensor Installations 44

3.3.1 Prior Work 44

3.3.2 Differences from Previous Work 45

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4.1 Overview of Methodology Employed 47

4.2 Potential Weaknesses of the Methodology and Justification 51 4.2.1 The Use of a Live-In Lab Instead of the User's Home 51

4.2.2 The Use of Object-Based Sensors 52

4.2.3 The Low Number of User Studies 53

5. System Design and Implementation 54

5.1 The PlaceLab 54

5.2 Object Usage MITes 55

5.2.1 Ideal Orientation 57

5.2.2 Power Optimization 59

5.3 Smartphone Sensor Installation Application 60

5.3.1 Introductory Material 60

5.3.2 Installation Rules and Object Placement Suggestions 62

5.3.3 Installation Process 63

5.4 Activity Training Example Acquisition Methods 69

5.4.1 Experience Sampling 69

5.4.2 Storyboarding 70

5.4.3 Play Acting 71

5.4.4 Professional Annotation 73

5.5 Review of Iterative Design 74

Chapter 6 76 6.1 Participants 76 6.1.1 Recruiting 76 6.1.2 Selection Criteria 77 6.1.3 Selected Participants 77 6.2 Pre-Study Interview 78

6.3 PlaceLab Move-In Procedure 79

6.4 End-User Sensor Installation Process 79

6.5 Professional Sensor Installation Process 80

6.6 Sensor Installation Documentation Procedure 80

6.7 Post-Installation Interview 81

6.8 Recording of Daily Life __81

6.9 Participant Photograph Recall 82

6.10 End-User Activity Example Acquisition 83

6.11 Post-Study Interview __84

6.12 Expert Annotation Procedure 84

7. Evaluation 85

7.1 Sensor Installation Analysis 85

7.1.1 Time to Install 88

7.1.2 Coverage 93

7.1.3 Optimal Technical Placement 100

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7.1.5 Long-Term Viability 102

7.1.6 Picture Recall 104

7.2 Activity Example Acquisition Analysis 107

7.2.1 Experience Sampling 107

7.2.2 User Labeling Methods: Storyboarding and Play Acting 110

7.2.3 Expert Annotation 112

8. Observations & Design Recommendations 114

8.1 In-Situ Knowledge 114

8.2 Goal Driven Installation 115

8.3 Education through Experience 116

8.4 Rapid, Understandable Feedback 117

8.5 The Need for a Recoverable, Robust, and Flexible System 118

8.6 The Balance of Practical and Technological Issues 119

8.7 Simplicity is Scalable 119

9. Conclusion 121

Appendix 1 : Experience Sampling Protocol 123

Appendix 2 : PlaceLab Wired Switch Placements 126

Appendix 3 : MITes Placements for Participant 1 129

Appendix 4: MITes Placements for Participant 2 135

Appendix 5 : HandLense Annotation Tool 142

Appendix 6: Phone Usage Quick Guide 156

Appendix 7: Sensor Installation Tool 158

Appendix 8 : Participant Storyboards 169

Appendix 8.1 Participant 1 169

Appendix 8.2 Participant 2 175

Appendix 9 : Participant Play Acting 183

9.1 Participant 1 183

9.2 Participant 2 188

Appendix 10 : Data Annotator Log 197

Appendix 11 : Annotated Activities 199

Appendix 12 : Pre-Study Interview 202

12.1 Participant 1 202

12.2 Participant 2 206

Appendix 13 : Post-Installation Interview 211

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13.2 Participant 2 220 Appendix 14: Post-Study Interview 231

Appendix 14.1 Participant 1 231

Appendix 14.2 Participant 2 236

Appendix 15: End-User Sensor Installation Times 241

Appendix 15.1 Participant 1 241

Appendix 15.2 Participant 2 244

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List of Figures

FIGURE I-1. THREE AREAS OF RESEARCH IN UBIQUITOUS COMPUTING RESEARCH...14

FIGURE 2-1. TOTAL EXISTING AND NEW HOME SALES IN THE U.S. BETWEEN 1980 AND 2005. ... 24

FIGURE 5-1. PLACELAB FLOOR PLAN. ...-. - - ... 54

FIGURE 5-2. MULTIMODAL SENSORS INTEGRATED SEAMLESSLY INTO THE PLACELAB INFRASTRUCTURE. SOURCE [43]...---...-... ----.... - - --- - ---- - -- - --- - -- --...55

FIGURE 5-3. (A) A MITES OBJECT-USAGE SENSOR (LEFT) AND ITS PROTECTIVE CASING (RIGHT), (B) EXAMPLE OF A MITES OBJECT-USAGE SENSOR BEING "STUCK-ON" A CABINET DOOR ... 56

FIGURE 5-4. SUMMARY OF OBJECT USAGE MITES DETECTION. ... 57

FIGURE 5-5. MITES ORIENTATION. (A) EXAMPLE OF THE OPTIMAL MITES ORIENTATION GIVEN THE DIRECTION OF MOTION OF A DRAWER, (B) SYMBOL DEPICTING OPTIMAL ORIENTATION FOR OBJECT USAGE MITES, (C) EXAMPLE OF THE NON-OPTIMAL MITES ORIENTATION GIVEN THE DIRECTION OF M O TIO N O F A D R A W ER . ...- - -. ---...---. 58

FIGURE 5-6. END-USER INSTALLATION TOOLS: (A) AUDIOVOX SMT5600 SMARTPHONE, (B) Box OF OBJECT USAGE M ITES, (C) ADHESIVE PUTTY USED TO ATTACH SENSORS...60

FIGURE 5-7. EXAMPLE SCREENSHOTS OF THE INTRODUCTORY MATERIAL PROVIDED BY THE SMARTPHONE INSTALLATION APPLICATION...---...61

FIGURE 5-8. SENSOR PLACEMENT EXAMPLES PROVIDED BY THE SMARTPHONE INSTALLATION APPLICATION. ... ... - . - - - .. 6 363...-- FIGURE 5-9. THE MITES SENSOR IDENTIFICATION PROCESS: (A) SELECT A SINGLE MITES SENSOR, (B) HOLD THE SENSOR STILL THEN SHAKE THE SENSOR TO BROADCAST THE SENSORS ID#, (C) VERIFY THE M IT ES ID #. ... ... ----.... ---... 64

FIGURE 5-10. SUMMARY OF THE SMARTPHONE SMART LIST FUNCTION...68

FIGURE 5-11. STORYBOARD ACTIVITY LABELING TOOL. EXAMPLE OF MAKING COFFEE...71

FIGURE 5-12. EXAMPLE OF THE PLAY ACTING SMARTPHONE APPLICATION...73

FIGURE 5-13. HANDLENSE APPLICATION: CUSTOM DATA VISUALIZATION AND EXPERT ANNOTATION TOOL DESIGNED FOR USE WITH THE PLACELAB AND MITES SENSORS. ... 74

FIGURE 6-1. EXAMPLE OF AN OBJECT WITH BOTH A PROFESSIONAL AND END-USER INSTALLED SENSOR...80

FIGURE 6-2. END-USER SENSOR PHOTOGRAPH RECALL APPLICATION...83

FIGURE 7-1. PARTICIPANT 1 SENSOR INSTALLATION FLOOR PLANS. A.) PLACELAB WIRED SWITCH SENSORS, B.) PROFESSIONALLY INSTALLED MITES SENSORS, AND C.) END-USER INSTALLED MITES SENSORS. .. 86

FIGURE 7-2. PARTICIPANT 2 SENSOR INSTALLATION FLOOR PLANS. A.) PLACELAB WIRED SWITCH SENSORS, B.) PROFESSIONALLY INSTALLED MITES SENSORS, AND C.) END-USER INSTALLED MITES SENSORS. .. 87

FIGURE 7-3. AVERAGE END-USER SENSOR INSTALLATION TIMES. A.) PARTICIPANT 1 AVERAGE TIMING DECOMPOSITION. B.) PARTICIPANT 2 AVERAGE TIMING DECOMPOSITION...90

FIGURE 7-4. A SENSOR PARTICIPANT 1 ATTACHED TO HER ANTIPERSPIRANT BECAUSE SHE THOUGHT IT WOULD BE FUNNY AND INTERESTING TO UNDERSTAND HER USAGE OF IT. ... ... 95

FIGURE 7-5. OBJECTS WHICH PARTICIPANTS DID NOT PLACE SENSORS ON, BUT MAY HAVE IF A CONCRETE APPLICATION FOR SENSOR USAGE WAS PROVIDED. ... 96

FIGURE 7-6. EXAMPLE OF AN END-USER REASONING AWAY SENSOR PLACEMENTS. ONLY ONE OF THE TWO DOORS (THE LEFT DOOR) HAD A SENSOR PLACED ON IT... ... 97

FIGURE 7-7. EXAMPLES OF OBJECTS ON WHICH SENSORS WERE PLACED DURING THE PROFESSIONAL SENSOR INSTALLATION, BUT WHICH WERE NEVER USED BY THE PARTICIPANT. (A) WASHING MACHINE. (B) CLEANING SUPPLIES ... ... 98

FIGURE 7-8. EXAMPLES OF PARTICIPANT'S ITEMS WHICH WERE CONSIDERED TO PERSONAL OR STORED IN A LOCATION CONSIDERED PERSONAL DURING THE PROFESSIONAL INSTALLATION. THE RESULT WAS NO PROFESSIONAL SENOR WAS PLACED ON THESE ITEMS, ALBEIT A PARTICIPANT SENSOR WAS PLACED ON THESE ITEMS. (A) PHONE CHARGER, (B) NOVEL, (C) TOOTHBRUSH...99 FIGURE 7-9. (A) EXAMPLE OF PARTICIPANT OVERESTIMATION OF SENSITIVITY OF MITES SENSORS. (B)

PROFESSIONAL INSTALLATION OF SENSOR MEASURING THE SAME INTERACTION THAT (A) WAS MEANT

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FIGURE 7-10. IMAGE OF COUCH SENSOR INSTALLATION. THE PARTICIPANT PLACED A SENSOR BETWEEN THE

LEFT SEAT CUSHION AND THE CENTER SEAT CUSHION. HOWEVER, NO SENSOR WAS PLACED ON THE OPPOSITE SIDE OF THE COUCH...101

FIGURE 7-11. (A) EXAMPLE OF AN INCONSPICUOUS, PROFESSIONAL SENSOR INSTALLATION. THE SENSOR IS UNDER THE SEAT OF THE CHAIR. (B) EXAMPLES OF PROFESSIONAL SENSOR INSTALLATION THAT HAVE NO OPTION BUT TO BE NOTICEABLE GIVEN THE FORM FACTOR OF THE MITES SENSORS. ... 102 FIGURE 7-12. EXAMPLES OF CREATIVE SENSOR PLACEMENTS, YET NOT REALISTIC FOR LONG TERM USE. (A)

FACIAL HAIR RAZOR. (B) HANDLE OF FRYING PAN...103

FIGURE 7-13. EXAMPLE OF SENSOR INSTALLATION THAT IMPEDED THE NORMAL USAGE OF AN OBJECT. IN

THIS CASE, THE SENSOR MADE A TELEVISION REMOTE AWKWARD TO HANDLE AND SET ON A HARD

SURFACE ... -- - - --...104 FIGURE 7-14. EXAMPLES OF AMBIGUOUS PHOTOS CAPTURED BY PARTICIPANTS...105

FIGURE 7-15. AN EXAMPLE OF DUAL SENSOR AMBIGUITY IN A PARTICIPANT'S PHOTOGRAPH. IT IS UNCLEAR

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List of Tables

TABLE 4-1. SUMMARY OF IMPLEMENTED PROTOTYPE SYSTEM... 48 TABLE 4-2. DATA COLLECTED DURING INSTALLATION PROCESS...50 TABLE 5-1. OVERVIEW OF THE INTRODUCTORY MATERIAL PROVIDED BY THE SMARTPHONE SENSOR

INSTALLATION APPLICATION... . .. ...- 61

TABLE 5-2. RULES FOR THE PLACEMENT OF OBJECT USAGE MITES... ... ... 62

TABLE 5-3. THE STEPS FOR INSTALLING SENSORS REQUIRED BY THE SMARTPHONE INSTALLATION

A PPLIC A TIO N...---... .---.... . ---... 64

TABLE 5-4. SENSOR INSTALLATION IDENTIFICATION ALGORITHM CRITERIA...65

TABLE 5-5. WALKTHROUGH EXAMPLE OF HOW PLAY ACTING TECHNIQUE WOULD BE PERFORMED FOR

"MAKING COFFEE" ACTIVITY. ... 72

TABLE 7-1. A SUMMARY OF THE TIME EACH PARTICIPANT SPENT READING INFORMATIONAL MATERIAL

CONCERNING SYSTEM SETUP. TIMES ARE PRESENTED PER FUNCTIONAL SET...89

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Chapter 1

1. Introduction

1.1 Context-Aware Applications in the Home

As sensors are miniaturized and devices such as home computers, mobile phones, and entertainment equipment increase in computational power, it may be possible to create computer applications for the home that make useful activities more fun, safe, or

educational. Computing applications have been proposed for the home to help with tasks such as motivating behavioral changes [1, 2], monitoring aging family members [3], remembering critical tasks [4, 5], tutoring [6], understanding decisions and behaviors [7],

mediating and sustaining communication [8], and improving human-computer

communication using gesture-based instructions [9]. Most of these applications exploit the ability of a computer use sensors to automatically detect characteristics of what a person is doing, so that the computer can respond in a natural and non-burdensome way. Systems that use sensors to infer context and respond to it are called context-aware[ 10].

Context-aware ubiquitous computing [10, 11] provides the promise of sensing technology seamlessly integrated into every environment [12], potentially freeing occupants from mundane everyday tasks and allowing them to focus on new, more compelling goals[1 1]. Context-aware systems may also allow people to interact with computers in ways that are more natural than the keyboard and the mouse, such as through moving, talking, and gesturing [13]. As Mark Weiser remarked, ubiquitous computing provides a vision of "machines that fit the human environment instead of forcing humans to enter theirs ... "

[11]. If the proposed systems become a reality, they have the potential of greatly

enhancing home life, enabling occupants to refocus on the important aspects of home life such as family, education, relaxing, and fostering personal relationships.

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Researchers in ubiquitous computing and context detection hope to provide the context-detection tools required to widely deploy context-aware systems in homes. However, before innovative context-aware applications are widely adopted outside of the laboratory

environment, researchers must overcome a number of challenges in order to make the context-detection technology commercially viable. A context-aware system usually consists of these components:

" Multiple sensors that are placed in an environment. Often they are installed on objects of interest.

* Software that collects and processes sensor data to infer activity and context. The way that the sensors are placed in the environment can have a significant impact on the accuracy of these algorithms.

" A set of training data that is used by the inference software. Training data usually consists of a set of example sensor traces for known contexts of interest that need to be detected. The training data is fed into the inference software in order to customize the recognition so that it will work despite differences between individuals and environments.

The cost of the sensors, while currently a concern, will increasingly drop. Many

researchers have argued that the cost of wireless sensors will drop so low that it will be possible to distribute hundreds throughout a single environment [14]. Even now, for just

several hundred dollars it is possible to place RFID tags on hundreds of objects in a home and use a special bracelet to detect when someone's hand is near an object [15]. Soon it will be cost effective to place other types of sensors, such as accelerometers that detect when objects are used [16, 17], on many devices as well.

The cost of software, once it is developed and tested, is also quite low. Therefore, the two greatest cost barriers to widespread deployment of context-aware algorithms may be 1.) the cost of hiring a professional to install the sensors, if a professional is required, and 2.) the cost (in time, money, and/or inconvenience) of acquiring training data that may be required to make the algorithm work.

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1.2 Sensor Installations: Questions Facing Context-Aware

Applications in the Home

Only recently have ubiquitous computing researchers begun to address the problem of how sensors that detect context might be deployed into the home environment by

non-experts [18]. It is still unclear how end-user sensor installation might impact the performance of a context-aware system.

This thesis explores the impact of an end-user sensor installation compared to a

professional sensor installation. Why is an understanding of this impact important? The cost associated with the sensor installation necessary in deploying a context-aware application may be the most significant factor determining the commercial feasibility of the application. In addition, installation constraints can impact the activity recognition algorithms both in performance and the procedures used in algorithmic setup and configuration.

The majority of recent work in context-aware ubiquitous computing can be classified in three areas: 1.) The viability and performance of sensor technologies [17, 19-21], 2.) the accuracy of activity recognition algorithms [22-26], and 3.) the design of application-specific interfaces [1, 5, 10, 27, 28]. Figure 1-1 represents a high-level, graphical

overview of these components and the interaction between them. The sensor technology (and installation) is strongly tied to the home itself - the size, layout, and objects in the environment. The application is used by the end user, who is living in the home where the sensors are placed. A change in how sensors are installed in the home can impact activity inference quality and, consequently, application usability.

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Domestic Context-Aware Ubiquitous Computing

Application Activity Labeling Sensor

Interface & Inference Technology

End User

Home

Environment

Figure 1-1. Three areas of research in ubiquitous computing research.

Sensors that are installed by professional technicians or researchers are likely to provide the best context-detection performance, but at a high labor cost. In addition, as behaviors or objects in the home change, the experts may need to continuously monitor and

maintain the system, at even greater expense. By having end-users install sensors on their own, this installation fee can be avoided. Moreover, end-users might be in a better

position to incrementally update the system on their own as circumstances change. Context-detection performance may suffer, however, if sensors are not placed effectively or maintained properly. In this thesis, the tradeoffs between expert and layperson

installations are considered. It describes the design and analysis of two exploratory, in-situ user studies. Based on the studies, design recommendations are proposed for systems

that eliminate the need for professional installation by having end-users install all the necessary sensor hardware on their own.

1.3 Approach

This thesis explores the following ideas:

1. Non-trivial End- User Sensor Deployment - In prior work an empirical evaluation of end-user sensor deployment was presented based on installation of mock sensors [18]. Here, we build upon that work but instead using functional sensors for object-based dense sensing [29]. We show how users can accomplish a complex task of installing hundreds of sensors throughout the home environment with an easy-to-use phone interface.

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2. Naturalistic Installation Process - In the study presented here, the sensor installation process was performed by two subjects in a real home. Unlike

previous studies, no researchers were present during sensor installation and no in-person training was provided to the user. All installation instructions were

provided via a mobile phone interface designed to be easy to use by a non-expert. The interface approximated how instructions might be presented by an actual commercial product.

3. Operational Data Collection using End- User Installed and Expert-Installed Sensors - The sensors installed by the subjects were fully operational. After completion of the end-user installation, a researcher (expert) installed a second sensor for each one installed by the end-user (as well as additional sensors in other useful locations) so the two installations could be compared. Data was collected simultaneously from both installations as the subjects lived in the home. 4. Longitudinal, In-Situ Study - Two user studies were conducted in which the

participant lived in a fully functional real apartment located in a residential condominium complex. The studies lasted seven and nine days respectively. Data was collected during the entirety of both stays for analysis purposes.

5. Empirical Analysis of Several Installation Methods - Two professional sensor

installations and one end-user sensor installation were qualitatively and quantitatively compared for each subject's stay in the home.

6. Qualitative Evaluation of Sensor Installation - Details (and unexpected behavior)

from analysis of sensor data (including audio-visual data of the installation process) is reported.

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Chapter 2

2. Sensor Installations

Environmental sensors placed throughout a home can be used by ubiquitous computing systems to infer context. However, the introduction of any new technology into the home must not unduly disrupt the rhythms of the household. In this work, we assume that end users will find value in some of the context-aware applications that have been proposed in the past [2-5, 10, 18, 27, 30-34] if the context-detection can be achieved without undue

disruption. End-users must perceive the opportunity presented by the system to improve their daily lives as greater than the demands and inadequacies of the technology [35].

One place the technology can fail is by requiring a high installation cost in terms of dollars or inconvenience. This chapter presents an overview of three approaches to sensor installations: two professional approaches and one user approach. Although an end-user sensor installation approach has advantages over the professional approach, it also creates challenges.

2.1 Defining the Sensor Installation Process

In this work, a sensor installation is defined as the deployment of sensors and software in the home environment necessary for inferring context. Depending on the sensors used and the installation approach, the effort and cost of the deployment can vary widely. However, regardless of the specific instantiation chosen, the sensor installation process consists of four steps: the purchase of the sensors, the placement of the sensors, the

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2.1.1 Sensor Purchase

Depending on the method of sensor installation, different distribution methods are possible once domestic context-aware systems become commercially available.

Distribution models proposed range from the user purchasing a pre-boxed kit from a local retail store to the purchase of a custom designed, professionally managed sensor

deployment administrated by a service provider at the time of home construction. The cost of most sensor hardware being used in ubiquitous computing research (wireless accelerometers, microphones, cameras, etc.) will drop quickly if the devices begin to be mass produced at high volume.

2.1.2 Sensor Placement

How sensors must be placed depends upon the type of sensors and algorithms being used to detect context. Systems that use computer vision processing and camera sensors, for example, may require experts to place the cameras in ways such that secularities and highly variable light sources (e.g. windows) are not in camera views. As an alternative to

using a small number of complex sensors such as cameras or microphones (which may provoke privacy concerns), object-based dense sensing can be used. Object based sensors used in past work include RFID tags [22, 23, 26, 36, 37] and accelerometer-based object usage stick-on sensors. As an example, placement of object-usage sensors consists of selecting an object to which a sensor should be affixed (e.g. trash can) or the location

(e.g. in the living room ceiling), selecting the location of the sensor on the object (e.g. on the upper right corner of the trash can lid or center of ceiling), and selecting the

orientation of the sensor (e.g. what are the ideal settings for the sensor). Each sensor placed may have an optimal orientation (e.g. the sensors antenna may need to point towards the sky for maximum range or a camera may need to point at a forty-five degree angle from the ceiling) or location that is ideal for its performance (e.g. an accelerometer is more responsive when placed in a way that the direction of motion is axis-aligned with the accelerometer or a camera needs to not be pointed at a sunlit window). Any

placement that deviates from the optimal placement of that sensor can negatively impact the functionality of that sensor and, in turn, the performance of the system.

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2.1.3 Sensor Labeling

Sensor labeling is a semantic association of a sensor with an object or space. Sensor labeling may be simple (e.g. Sensor ID #345 is labeled as the "Telephone"). However, more complicated labeling may be required by the system, such as associating object or location information with a sensor (e.g. Sensor ID #180 is attached to a "Toaster oven" and is located in the "Kitchen," or Camera 1 is a view of the "Bedroom"). As described later in this work, entering a text label may be the best information for the algorithm to use, but this may increase installation time and complexity. The semantic association provided by sensor labeling may be used for a variety of purposes. One use may be to communicate with the user about system state. For example, the system may need to alert the user that the battery powering sensor #345 is low. Another use of sensor labels is to obtain information from the user in order to gain a better knowledge of how people behave in the environment [24, 38]. For example, the semantic information provided by

sensor labeling may not be used to communicate with a human at all, but instead used internally to help improve recognition of activities [25, 37]. Questions raised by the sensor labeling process include: 1.) When should information be associated with each sensor?; 2.) What method or interface should be used to associate the information?; and 3.) Who should be responsible for associating the sensor with the corresponding labels? Different answers to these questions have different tradeoffs on installation complexity, which are explored throughout this work. No matter what technique is adopted, the installation labeling quality and level of detail may impact both the usability of the system and the system's recognition accuracy.

2.1.4 Gathering Examples and Building Models of Activities

To build useful computational models of the user's daily activities, machine learning techniques often need some form of prior information about the user. This type of prior information may be in the form of hand-coded rules (section 3.3.1), training examples used to build probabilistic models (3.3.2), or training examples and rules extracted from

existing knowledge bases (section 3.3.3). Obtaining useful prior information can be problematic in each case, especially in the home environment where activities are highly varied and personalized and disruption is not readily tolerated. The efficacy of different

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methods of obtaining this information is dependent, at least in part, on the sensor installation process chosen.

2.2 A Scenario

A scenario can help to highlight the differences between the professional and the end-user sensor installation approach. It will also help illustrate key concepts presented later in the document.

Imagine that the goal is to design a simple context-aware domestic ubiquitous computing system to detect when the occupants are sitting at the dinner table and actively eating dinner. If an "important" call is detected by the system, the call is connected to the user at the dinner table. However, if the call is of marginal importance or disruptive to the

family's communication during dinner, the call is diverted to a voice mail system and the user is notified of the missed call once dinner is complete. By detecting the "sitting at the

table eating dinner" context, the system is able to suppress phone calls from unknown sources thus filtering the many marketing calls that are placed to the home during dinner. For a human assistant, undertaking such a task would be somewhat trivial. The assistant would come into the house, recognize when dinner has started, and begin to screen calls. The obvious difference between important and unimportant calls would be recognized immediately and the more subtle distinctions would be learned as the assistant gained

experience with the family. However, for a digital system the challenge is to sense the "right" information needed for common sense decision making. For example, how will the sensors required to detect the dinner context get deployed in the user's home? If the call monitoring application in the scenario employed a professional sensor approach, the user would call the company that sold the application and purchase the service including the installation, similar to the way home security systems are currently deployed [39].

Once the service is purchased a professional technician would schedule a time to come to the users home to install, label, and test the sensors and the supporting software.

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An end-user sensor installation approach changes the scenario. In that case, the user would use an online or local vendor to purchase a call monitoring application kit. The kit

would include in deployment instructions, sensors, and any additional software or computing system needed. The decision of where and how to place the sensors is completely decided by the user.

2.3 Two Approaches to the Sensor Installation Process

The burden of the sensor installation task may be assumed by either a team of

professional installers or by the end-user themselves. The approach taken is a decision that effects requirements placed on the entire system and the user's perceived value of the final application. Two methods of introducing sensors into the environment exist: 1.) The user hires a company to install the sensors and technology into the home environment; 2.) The user obtains the sensors and technology and installs the sensors and technology themselves. Each method is introduced in terms of its scenario and subsequent sections address how different properties of the overall system are influenced by each of the two methods.

2.3.1 The Professional Sensor Category

A professional approach to sensor installations means that experienced professionals (or researchers) are responsible for deploying the context-aware ubiquitous computing final application. Experts are usually good at selecting where and how sensors must be placed because they know the details of the how the sensors and algorithms work. This

''optimal" sensor placement increases the likelihood that the application will perform as desired. This approach to sensor installations is the most widely adopted for research prototypes.

One downside to professional installation is that the end user is not involved in where and how sensors are placed. This lack of involvement may be more likely to ultimately lead the user to misunderstand and or be skeptical of the system, especially when performance of the application is poorer than that anticipated.

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Retrofitting a home for ubiquitous computing sensing would be much like having a expert technician install a home security system. A trained technician is sent by a service provider to install technology into the subscriber's home. In the scenario, the head-of-household would call the ubiquitous computing provider, purchase or lease the necessary sensor equipment, and in some cases purchase a monthly service. The ubiquitous

computing technician would then come into the consumer's home and augment the environment with sensors, set up the necessary software infrastructure, and test the system to verify operation and insure calibration settings are set properly [40]. A myriad of tests using specialized tools and well-practiced processes could be used to insure the system is performing properly.

This method allows for customization of the sensing to the environment. The necessary information for per-home personalization could be achieved via an initial survey and walkthrough conducted by the technician. This would most likely be conducted to

determine how many sensors are needed, what types of sensors will work best if multiple sensor types are available, and where the most favorable locations for each sensor

placement are. The initial walkthrough could also be used to manage user expectations about what is necessary for deploying the system, and the technician may be able to explain the advantages of various sensor locations. The technician would also explain details concerning the expected state of the house (such as how clean each location should be) to facilitate the team that would install the sensors and the system. After this walkthrough, the team of technicians will enter the home and take several hours, possibly several days, installing, calibrating, and diagnosing the system. The user will then likely be given a quick overview of the system and a telephone number for a customer support department. Based on current rates for home security installation [39], it is unlikely this would cost less than $40 per hour of effort. The hardware and installation cost could be absorbed into a monthly fee over the period of a several year contract, similar to how

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2.3.2 The End-User Sensor Installation Category

An end-user approach to sensor installations shifts the responsibility of the installation to the user. This shift increases user involvement, opening the potential for leveraging

knowledge of their domestic behavioral patterns and increasing comfort with the system. It can also make non-critical applications feasible that were not viable due to the high cost of professional sensor installations. Unfortunately, the lack of a complete

understanding of the system by the user increases the chance that the installation is less than optimal. This puts a greater burden on the design of sensors and the context-aware algorithms to overcome the potential shortcomings related to end-user installations.

A familiar analogy for the end-user approach is a consumer who goes to their local retail store and buys a wireless network router for their household high-speed Internet service. The router is installed by the user, who is relying on the provided instruction manual and computer software. In the ubiquitous computing scenario, the end user would go to their local retail store and purchase a box of sensors. The system could come with software for

their mobile phone, which might be used as a convenient reference aid while moving about the home installing sensors. The proper placements and orientations of each sensor are completely determined by the user. This responsibility, paired with the number of sensors needed for the object-based dense sensor approach, puts demands and constraints on sensor design that the other methods do not. This is because the handling of potential errors must be shifted from the installation phase to the sensor design phase of the system.

2.4 Cost

The pursuit of a widely deployable context-aware application requires analysis not only from a traditional research perspective, but also from the perspective of a business proposal by considering tradeoffs such as cost [41]. Designing systems to meet the unique demands of the home must balance the perceived value of the application with the monetary cost of system deployment. The cost incurred by professional installations may be appropriate for critical health monitoring systems in the home where it is mandatory that the system perform optimally. However, such professional installation methods may

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be too costly for other more "frivolous" applications [18]. If the only choice for sensor installations is the professional approach, applications such as the telemarketer screening scenario presented in Section 2.2 may never be justified when confronted with the cost of deployment - even if the cost of the required sensors is negligible. On the other hand, the professional approach has the advantage of low effort and little time commitment from the end-user. Thus, a system designer must carefully balance the inversely related variables of user cost and user involvement. This section outlines how each approach to sensor installation influences this balance.

2.4.1 Purpose-Built Context-Aware Enabled Houses

The cost of a system pre-built into a new home would probably be integrated into the cost of the home. After accounting for any costs due to design, the core costs of these systems is the wiring of the house structure, the cost of the sensors themselves, the cost of skilled labor for installation, and the cost of upgrades and general maintenance. In 2002, the

Center for Building Environments estimated that the cost of wiring added 50% - 90% to

the cost of the sensors being deployed [42]. This is non-trivial since purpose-built environments such as the PlaceLab have over a mile of wire and over one hundred sensors[43]. This cost would also include a per annum service charge as in the

professional installation method or the user responsibility for maintenance and calibration as presented in the end-user sensor installation method.

While the possibility of purpose-built context-aware enabled research houses that have this level of integrations, such as the PlaceLab [43], may exist in the future, current homes are not designed to support such awareness and likely will not in the immediate future. Even if the home building industry was retrofit for context-aware applications and producing such purpose-built homes, there is still the conundrum presented by existing homes. According to the National Association of Realtors, the yearly sale of existing homes outpaces the sale of new homes nearly 6-to-1 (Figure 2-1), and this ratio is predicted to increase through the next several years [44]. This trend supports Edwards and Grintner's observation that it seems more practical that homeowners will choose to "upgrade" their homes by bringing the necessary technologies into their homes in a

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piecemeal fashion [45]. This "upgrade" is more easily supported by the next two installation methods.

Existing Home Sales

New Home Sales

tn n11on unfts L, Totali exs~nq home salesintosd rsrj oalewhmsas

Soure:MAR Source; US Cenus

Figure 2-1. Total existing and new home sales in the U.S. between 1980 and 2005.

2.4.2 Professional Sensor Installations

The cost for a cable television technician to visit a consumer's house, not including the cost for any devices or data services, costs about USD $40 [46] per visit necessary. Recent financial models of proposed sensor-based, critical task systems such as Elder Monitoring systems and Home Security systems have estimated an annual cost of USD $7,500 - $8,000 to the user [47]. This figure includes personalized design, installation,

service, and maintenance. It is clear from these estimates that at least some non-critical ubiquitous applications must rely on an end-user sensor installation method in order to be financially feasible to the majority of consumers.

The professionally installed system will place only a small initial burden on the user in terms of the walkthrough and accommodating the installation team. Experiencing this invasion of a large part of the home by technicians may be overwhelming for users who view this process as an undue foray into their privacy, who are concerned about damage

that may be done to their possessions by the technicians, or who are not tolerant of the possible disruption of the delicate nature of their domestic organization caused by a full

house installation. Conversely, some users may view these concerns as a reasonable price paid for the benefit of the system and the convenience offered by a professional

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installation. Whatever the potential impact on the user, current methods of conducting domestic computing research most readily map to this approach of sensor installations.

2.4.3 End-User Sensor Installations

The end-user sensor installation reduces cost of installation by shifting the responsibility for deployment, calibration, and management of the context-aware system to the end-user. The reduction in cost may make some non-critical context-aware applications more feasible, thus potentially having a wider impact in the home market. However, the increased reliance on an inexperienced user to perform the sensor installation also places more requirements on the system design to reduce the complexity of the entire process.

By reducing complexity, the potential of user frustration and deployment errors is also reduced. Thus, the benefit of the cost reduction of end-user sensor installations must be considered jointly with the needs of the system and the additional demands placed on the user.

One example of the additional requirements placed on the user is the time taken to perform the installation. If the installation of each sensor takes 3-5 minutes, a dense object sensing system that needs 100+ sensors to reliably infer context will take the user between 6 and 10 hours to perform. The designer must, then, determine if the user will think incurring this start-up effort as reasonable given the benefit the application

provides. If the start-up costs will not be seen as reasonable by a number of target users, the application will fail to make any significant impact in the home if it uses the end-user sensor installation approach. Some applications may permit incremental installation [48]. To reduce the setup effort, the end-user sensor installation approach should be coupled with sensors that have been specifically designed for ease-of-use. This implies that the calibration of sensors should not be tedious and the proper placements and orientations should be easily achievable. The unique requirements of an end-user sensor installation limits the type of sensors that can be used with the context-aware system and illustrate why it is imperative to choose the sensor installation approach early in design.

Conversely, such influence on the user's perceived value of the system makes it necessary to understand the impact of the sensors chosen.

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2.5 The Dynamic Nature of the Home

Cost is just one of the tradeoffs presented by different sensor installation approaches. A user's interaction with a domestic context-aware ubiquitous computing system will change over time as user needs evolve and changes are made to the environment. For example, schedules change when a child attends school in the autumn versus when that child is not actively in school during the summer. In terms of the scenario in Section 2.2, this type of change may substantially influence the way in which dinner is eaten. Thus a context detection system will need to cope. In order for domestic context-aware systems to be valuable to a user, they need to respond to the dynamic lifestyle of that specific user. It is probably impossible for the context-detection application developer to

anticipate all the ways in which domestic ubiquitous technology will need to adapt to changes in user behavior and environments.

The designer of context-aware systems must therefore make a decision that impacts system's ability of coping with change and how much the user may help with the adaptation process. The sensor installation approach influences how well the end user is able to convey to the system that certain changes in lifestyle have taken place. This is because each sensor installation provides the user a differing level of understanding and control.

2.6 Understanding and Control

User control has long been recognized as important in the design of user-friendly desktop applications[49]. Providing users with a control over the system may help them maximize the utility of the system by leveraging user expertise. It may allow the system to evolve with the lifestyles of the home, help the user gain trust in the system, and manage user

expectations about the system performance. Control is often expected by most users in their homes [8], causing them to become frustrated or confused when unable to affect how the system operates.

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In order for users to effectively wield any control provided by the system, they must have at least some understanding about how the control they exert impacts the system.

Unfortunately, the lack of user involvement in professional sensor deployment does not allow the user to build what Donald Norman referred to as an accurate mental model of the system [50]. Further complicating an understanding of the system is that many of the algorithms used for inferring context are not interpretable by the algorithm designer [51], much less the user. The combination of the installation technique, which augments the user's environment without much user involvement, and the lack of visibility offered by inference algorithms used provides the user little opportunity to understand how the system works. In the eyes of the user, the whole system is in danger of being viewed as a "black box" that monitors their actions and "magically" provides suggestions. Lack of understanding can cause the user to become dubious and distrustful of the

recommendations presented by the system [52]. Further, a lack of understanding eliminates the help the user may provide when the system inevitably makes errors or is unable to cope with the dynamic nature of the home.

End-user sensor installations potentially provide the user with more control and understanding by giving the user responsibility for the sensor installation process. However, the additional control increases the risk that the user will make errors and a non-optimal installation will be performed, thus reducing system accuracy. The subsequent sections in this chapter present the opportunities provided by involving the user in end-user installations and possible pitfalls that accompany each opportunity.

2.7 Adapting to the Home

The primary difference between a professional sensor installation and end-user sensor installations is how the sensors are introduced into the environment. This section presents a few of the opportunities and some of the additional challenges associated with end-user

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2.7.1 End-User Sensor Installations: Advantages

In addition to the benefit of reduced cost, end-user sensor installations offer several opportunities for increasing the usability and accuracy of context-aware systems.

2.7.1.1 Potential for Increased User Understanding

The hands-on nature of end-user sensor installations creates an opportunity for the user to become comfortable with the technology being introduced into the home. Not only does the practice give users a chance to handle and inspect the sensors, but the task of

installing the system may provide the user with a feeling of "ownership" for the whole system. Ownership is a result of the increased user control and can promote creativity in

sensor placements [53]. Additionally, the necessity of instructing the occupant how to properly install their sensors provides a means by which the user may learn how the sensors work. For example, in the scenario in Section 2.2, the user may need to affix a

sensor to each of their dining room chairs. Instead of just telling the user that a sensor should be attached to their dining room chairs, the instructions may include a brief description about how the sensors detect movement and vibrations of household objects. This description can help the user better understand how to place their sensors. It also may help the user to gain a better understanding, and therefore comfort, with how the sensors work. More exposure to the sensors paired with increased understanding and ownership may serve to quell some fears the user may have about the sensors and the

system.

2.7.1.2 Comfort and Privacy

In the scenario in Section 2.2, the goal of the system is to recognize eating dinner. While the occupants may be comfortable having sensors distributed around the kitchen and

dining room area, they may feel embarrassed or "judged" by sensors that are placed in the bathroom area. This is not because the users are embarrassed that they wash their hands before dinner, but perhaps because they are not always diligent about washing their hands

after using the bathroom during other times in the day. The occupants may feel that the sensors in the bathroom will be used to scrutinize their deviations from "acceptable" practices.

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The freedom afforded by the end-user sensor installation approach enables the user to choose what sensors are "judgmental" and thus exclude them. While this is possible with the professional user installation, the user control of the end-user sensor approach allows the user to change their mind. It is possible that the user could become more comfortable with the system the longer it is in the home. The user's comfort level may change

dramatically from when the system was first deployed. The end-user sensor installation gives the user the freedom to refine sensor placement based on changing comfort levels.

2.7.1.3 Domain Expertise

Ubiquitous computing researchers and technicians have an intimate understanding of the sensors and technologies that enable domestic ubiquitous computing systems, but no one knows the behavioral patterns of specific home more intimately than the individuals who

live and interact in the home on a daily basis. The occupants of a household are the domain experts of that environment [18], and designed expert knowledge for a task until actively engaged in it. For example, most adults who drink coffee would easily be able to outline the basic steps they take to make a cup of coffee in a given location. However, it is often not until that individual is actually making coffee that they remember the minutia that makes the whole process their own as opposed to their co-workers. These

infinitesimal details can be the key to effective algorithmic discrimination between activities performed in the home. Unfortunately, the cost and inconvenience of reconfiguring a professionally deployed system may make many such realizations superfluous to the system once installed. Conversely, one benefit of the professional sensor installation over the end-user installation is that if substantial changes or updates are needed, the burden and stress associated with these changes fall on a professional and not on the user. Large changes are therefore more likely to happen in a timely manner

since a homeowner's "do-it-yourself' projects usually take a weekend or two to complete once time is scheduled for the project in the user's hectic schedule.

End-user installations provide a solution to the need for a technician by empowering the user with the necessary knowledge and comfort with the technology to reconfigure the

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system at will. If the user finds a forgotten step in the execution of an activity, the user can then reconfigure the system to account for the addition. This ability to reconfigure enables the system not only to exploit specific domain knowledge that is rediscovered through performing the daily routines, but to also actively reconfigure and rescale the system based on the dynamic nature of the home.

2.7.1.4 Scalability

In addition to supporting personalization of the system to the occupants, context-aware ubiquitous computing systems also need to scale and evolve as behavioral patterns change. Support needs to extend past just the expansion of the system itself. It must include dealing with changes in life that effect the behavioral patterns of the occupants such as losing a job, kids going to school, or a new baby. Even seemingly trivial changes in the environment, such as adding a new technology or object that is not directly related to the ubiquitous computing system, can have an impact on the behavioral patterns of the environment that are significant to the performance of the ubiquitous computing system. A simple example of this is to introduce a new food processor to a kitchen. The addition of this common household item by the user is just one instance of the continually

evolving nature of the home. In response to the introduction of the food processor, the ubiquitous computing system must be able to scale upward in order to accommodate any changes in behavioral patterns that may result from the user cooking with their food processor. For object-based dense sensing systems, this means that a new sensor may

need to be placed on the food processor object and that the inference algorithms should begin to update the models that have been built for the "cooking" activity. This simple example suggests that at many times through its lifespan the system will need actively reconfigured to adapt to a multitude of new tasks [41]. For all the reasons addressed in the previous section, the professional sensor installation approach has difficulties handling such alterations. On the other hand, the properties of the end-user installation approach make it suitable for reconfigurations required to keep pace with a user's life. For example, a user might be able to add a sensor to the food processor when he or she notices that it is used during their cooking routine.

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2.7.1.5 Maintenance

Another benefit of the user control provided by the end-user sensor installation is the ability of the user to perform routine maintenance on the sensors. This may include simple tasks such as replacing sensors that have been dislodged from their original location. Unlike office environments, which have teams of administrators to handle the maintenance of complex systems, the home only has its occupants. While maintenance may pose an additional burden to the end-user, it is better for system performance and, once again, reduces the final cost of the system. For example, if wireless sensors are being used, batteries will need to be replaced. One option for the professionally installed

system is to have scheduled maintenance calls for sensors when technicians replace batteries. However, in between maintenance visits inoperable sensors may cause

degradation in system performance. Users who install sensors themselves may be comfortable changing the batteries themselves as needed, thus maintaining system performance. The same is true if sensors are dislodged. Users of professionally installed

systems may be fearful of replacing sensors themselves, even if they have the knowledge to do so. Conversely, users of self-installed systems will have experience installing sensors and may have the knowledge and comfort level to replace dislodged sensors.

2.7.2 End-User Sensor Installations: Challenges

The adoption of an end-user sensor installation approach offers the potential for the numerous advantages described. However end-user sensor installation is also susceptible to two pitfalls attributed to shifting responsibility to the end user.

2.7.2.1 User-driven reconfiguration

A user who self-installs a sensor system that is easily reconfigured may on occasion decide to exercise his or her control and reconfigure the system. This sensor

configuration could confuse inference algorithms. On the one hand, the system must be designed so the user feels a sense of control and feels comfortable configuring the system using his or her domain knowledge, thus it must be reconfigurable by the end-user. On the other hand, the system must be designed so that when changes are made to the sensor

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installation, these changes are communicated to the inference algorithm and handled appropriately. It is possible, for instance, that a small change to a sensor location would require a large amount of additional training data to be collected or impact the system's

performance in unanticipated ways.

2.7.2.2 Increased Potential for Sensor Deployment Errors

Regardless of the care put into the design of sensors and the process for use in end-user sensor installations, end users will make decisions when installing sensors that designers did not expect. Problems that can be anticipated and must be overcome include forgetting to place sensors on key objects, placing sensors on locations or in orientations that do not achieve the best sensor performance, or placing or orienting the sensors in ways that overestimate the performance capabilities of the sensors.

Anticipating the potential for such errors, care must be taken in writing instructions to help the user understand the shortcomings of the technology and the installation techniques to account for these shortcomings. Nonetheless, the cryptic nature of the intricacies of the sensors and algorithms of the system as viewed by the end-user will result in mistakes being made during the installation process. These inevitable mistakes will have an impact on the performance of the activity recognition algorithms chosen. As a result, the reduced performance due to errors must be weighed against the other benefits presented by end-user sensor installations.

2.8 Inferring Context: Teaching the System

Invariably context-detection inference algorithms must encapsulate knowledge about the user activities of interest and the domain in which the user is performing these target activities. As with the other dimensions presented, the manner in which the system learns to identify user activities and knowledge is imparted to the system is impacted by the

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some opportunities over professionally installed systems when training examples and domain knowledge must be provided.

Most context-aware systems have difficulty effectively inferring context due to the ambiguity in user tasks and the amount of multi-tasking that happens during a typical user's daily activities. As Holmquist and his colleagues remarked, users tend to perform their goals in "small bursts" of activities, "often extended throughout the entire day and in many different places" [54]. In practice, the activity examples necessary to train and test context-aware system are discontinuous and full of ambiguity, where examples of an activity are rarely both pure and complete.

2.8.1 Rule Specification

Rule-based systems (RBS) provide a means to "automate problem-solving know-how, providing a means for capturing and refining human expertise" [55] .RBSs attempt to incorporate practical human knowledge in conditional statements that modularize the knowledge encapsulated into atomic rules. Conditional statements are one of the most basic concepts in computer programming. For example, here is a simple rule:

IF a = 1 and b = 2 THEN class = a

RBSs compose a number of these simple rules in order to automatically create

generalizations useful for the categorization of activities based on observed sensor data. The resulting RBS architecture consists of a Knowledge Base and an Inference Engine. The knowledge base contains rules, which are problem-specific relationships between known facts, and any exceptions to the rules. The knowledge base in the Inference Engine is logic which handles the details of selecting rules that are important, how conflicts between rules should be handled, and what prediction should be produced based on the combination of facts and rules.

Traditionally, three people interact with a rule-based system: The end-user who uses the system, the domain expert who provides the expertise about the domain and creates the rules, and the knowledge engineer who molds the domain expertise into a usable form and maintains the interaction between the inference engine and the knowledge base

Figure

Figure 1-1.  Three areas of research in  ubiquitous computing  research.
Figure  2-1.  Total  existing  and  new  home  sales  in the  U.S.  between  1980  and  2005.
Table  4-2.  Data collected  during installation  process.
Figure 5-1.  PlaceLab  floor plan.
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