Experimental protocol

In order to estimate the robustness of the approach, during our experiment phase, activitiesA₁,A₂andA₃are realised and observed a huge number of times by introducing the following variations:

2.2. Ambient Assisted Living Test Area (AALTA)

2017-04-04 10:11:09 e22

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2017-04-04 10:11:21 e₃₁ 2017-04-04 10:11:32 e23

2017-04-04 10:11:32 e30

2017-04-04 10:11:43 e₃₅ 2017-04-04 10:12:01 e34

...

Figure 2.2.12: Example of AALTA logs.

• the insertion of noisy events (i.e., events not linked with the performed activity) during their realisation, for instance by wandering in the flat;

• some actions are interrupted;

• the execution order of elementary moves composing actions is changed;

• the action make tea is realised by two different ways: using the kettle or boiling water with hotplates.

These variations focus the majority of possible noisy phenomena occurring during a normal human life.

These activity realisations, split in different parts, compose a library of modular activity performance. By ordering them in different order and following different rules, it is possible to test several particular situations. On the one hand, to realise simple test, we can use activities performed without noise timely place in an optimal order.

On the other hand, to test more difficult situations such as interrupted activities or a direct succession of activities, we can use all kind of activity performance timely closed or broken.

In order to run our methods onto an acceptable dataset, a learning sequence was creating using a library containing realisations of activities to monitor. More precisely, this database is generated using recorded activity instances placed in a random time order and separated by a random number of random noisy events not belonging to the performed activities. The resulting sequence is composed of 2087 events corresponding to twenty realisations of each activity. This database generation method is presented in figure2.2.13.

First, to validate our methods, the activity recognition will be run over the learning database to verify that we are able to recognise the activities used during the discovering phase. Then, additional tests are run to analyse the robustness of our methods to successions of activities. For all those experiments, the details about the placed activities and their positions in the constructed database are kept. This information is used to compare the methods results to the reality. This knowledge is for the validation process only and is not needed for an application in the real world.

Chapter 2. Case Study

Figure 2.2.13: Structure of the test sequence of observed events

Conclusion

Since the topic treated in this thesis is directly linked to an actual societal problem needing concrete solutions, all methods presented in this thesis have to be applicable on a real case. The methods development cannot be decoupled with the technical application.

Therefore, in this chapter, several living labs were presented and their usability to apply our methods using their datasets was discussed. Unfortunately, the restrictions linked to the assumptions make in chapter 1 make the currently known living labs not fully adapted to our methods. These incompatibilities are mainly due to a lack of information concerning the equipped smart homes.

To deal with this problem, a new living lab was developed at the ENS Paris-Saclay.

This ambient assisted living test area (AALTA) is presented in section 2.2. Object and sensor placements are given and the expert decomposition needed in the activity discovery method (presented in chapter 3 of the manuscript) is detailed. Finally, the experimental protocol used to generate out test databases is developed and explained.

Chapter

Activity Discovery 3

"If you want to know someone, don’t listen to what he says, but look what he does."

- Tenzin Gyatso, His holiness the 14th Dalai

Lama-Abstract

This chapter presents a probabilistic approach allowing to model activities of daily living under the form of probabilistic finite-state automata. This method uses as input a log of events recorded during a learning phase and a hierarchical decomposition of activities to monitor to actions linked to the instrumentation of the dwelling. Finally, this approach is applied to the experimental smart flat to illustrate and validate its discovery method.

Contents

Introduction . . . 46 3.1 Models and notations . . . 46 3.2 A systematic procedure for models generation . . . 49 3.2.1 Generation of PFA structure . . . 49 3.2.2 Database of event logs exploration . . . 53 3.2.3 Probabilities computation . . . 55 3.3 Application to the Case Study . . . 61 3.3.1 Generation of PFA structure . . . 61 3.3.2 Database of event logs exploration . . . 63 3.3.3 Probabilities computation . . . 67 3.4 Discussion . . . 73 Conclusion . . . 73

Chapter 3. Activity Discovery

Introduction

In this chapter, we present the first contribution of this thesis: a new activity dis-covery (AD) method. We need to develop this innovative approach because of our Assumption 4 incompatible with existing methods. Indeed, Assumption 4, stated in section 1.2.2, rejects the possibility of labelling activities actually performed by the monitored person during the learning period. Unfortunately, this information is usually one of the mandatory inputs of AD methods.

Moreover, Assumption 2 consists in treating information coming from environmental binary sensors only. Therefore, the method is free from performed activity knowledge and uses only environmental and binary sensors.

One major asset of this approach is its portability. Indeed, it is applicable to each smart home regardless of the inhabitant pathologies (e.g. a patient suffering from Alzheimer do not need to help the process).

This approach models each activity to monitor by probabilistic finite-state automa-ton (PFA). Lost information due to the rejection of the performed activity knowledge is compensated by the addition of a specific expert knowledge which gives a hierarchical decomposition of the activities into a set of actions observable using a given subset of events among the smart home ones.

First of all, a theoretical picture of this new method is presented with the definitions and notations that are needed for a better understanding.

Then this concept is illustrated with the study of a specific case that has been described in the previous chapter.