Activity discovery

First, ADL have to be modelled. When the modelled is generated by learning, we use the term of activity discovery defined as follows:

Definition 1.6 (Activity Discovery AD). (Cook et al., 2013) The activity discovery is an unsupervised learning algorithm to discover activities in raw sensor event sequence data.

In the literature, a great variety of methods using different inputs and outputs can be found. A brief review of the major methods is now developed. In coherence with the sorting made in subsection1.1.2, these methods are grouped by considering the semantic level of the sensors used. The semantic level of the generated models are also highlighted to improve the comprehension of the pros and cons linked to each method.

Inputs of high level of semantics

InForkan et al.(2014), authors model human behaviour using expert knowledge and vital sign sensors. In addition, authors skip the ADL discovery process by choosing not to use data learning but rather ontological models as visible in table1.1.3. Therefore, the human behaviour models of this method have a very high level of semantics since they correspond to very specific situations. With these models, it is possible to directly detect dangerous situation and quickly react in case of emergency. However, those models are fully constructed using expert knowledge, and therefore subject to human mistake and forgot.

InDuong et al. (2009), authors use cameras to detect the location of the inhabitant.

Then, by using expert knowledge, authors link different successions of locations with activities to generate hidden semi-Markov models (HSMM) (Rabiner, 1989). A hidden Markov model (HMM) is a stochastic model of a process with an underlying part con-sidered as non-observable. Furthermore, an HSMM is an HMM in which a duration knowledge is added. Figure1.1.5 shows the dynamic Bayesian network graphical struc-ture for HSMM with generic state duration distribution. At each time slice, a set of variables V_t = {x_t, m_t, y_t} is maintained where x_t is the current state, m_t is duration variable of the current state, and yt is the current observation. The duration mt is a

Chapter 1. Activities of Daily Living Issues and Objectives

counting-down variable, which not only specifies how long the current state will last, but also acts like a context influencing how the next time slice t+ 1 will be generated from the current time slicet.

Figure 1.1.5: DBN representation for a standard HSMM. Shaded nodes represent ob-servation. (Duong et al., 2009).

By performing known and wanted activities, authors use a Coxian distribution to efficiently model the duration information. This information added to the HSMM result in a novel form of stochastic model : the coxian hidden semi-Markov model (CxHSMM).

The discovery method presented uses expert-given HSMM as bases for the duration learning. According to the authors, the use of HMMs is suitable and efficient for learning simple sequential data. It is notable that, in this work, the information from cameras are quickly parsed to become a simple location information.

Inputs of average level of semantics

In Lara and Labrador (2013), the AD, called the training stage, initially requires a time series dataset of measured attributes from individuals performing each activity.

The time series are split into time windows to apply feature extraction and thereby filtering relevant information in the raw signals. Later, learning methods are used to generate an activity recognition model from the dataset of extracted features. Likewise, data are collected during a time window, which is used to extract features. Such feature set is evaluated in the priorly trained learning model, generating a predicted activity label (see figure1.1.6).

A generic data acquisition is also an identified architecture for AD and AR systems, as shown in figure1.1.7. In the first step, wearable sensors are attached to the person’s body to measure attributes of interest such as motion, location, temperature, ECG, among others. These sensors should communicate with an integration device (ID), which can be a cellphone, a PDA, a laptop, or a customised embedded system.

The models thus obtained can be probabilistic or not. However, the need to split the data recorded during the learning period to "individuals performing of each activity"

leads to record labels of the performed activity.

1.1. Literature review

Figure 1.1.6: General data flow for training systems based on wearable sensors (Lara and Labrador, 2013).

Figure 1.1.7: Generic data acquisition architecture for Human Activity Discovery and Recognition (Lara and Labrador, 2013).

Inputs of low level of semantics

In Saives et al. (2015), authors propose a method to model, starting from a log of binary sensor events (rising and falling edges), the habits of the inhabitant. These models are extracted by sequence mining techniques and modelled by extended finite automata (EFA). The learned habits are then labelled by an expert. This AD method, base on theAgrawal and Srikant(1995) pattern mining method, is a black bock discovery method. However, as this pattern mining method distinguish each recurrent pattern, each aleatory and minor event inversion create a new pattern. The expert work is thus fastidious if treating data from a big smart home. As an output, authors gives a global map of activities represented by an EFA. This output model has a medium level of semantics since the main interesting part (i.e. the labelling) is donea posteriori by the expert.

InCook and Krishnan(2015), authors presents several machine learning using binary sensors and individuals performing of each activity as inputs. Naîve Bayes classifier, Gaussian Mixture model, hidden Markov model, decision tree, support vector machine conditional random field are probabilistic models possible to generate with these inputs.

The computed models have semantically high information since they are trained directly with adapted and labelled data.

Chapter 1. Activities of Daily Living Issues and Objectives

The majority of the existing methods model ADL by probabilistic models. In ad-dition to their natural robustness to small variations, those models have the advantage to be coherent with the human non-determinism. Therefore, in this thesis, a modelling of human ADLs by probabilistic model is preferred. However, when explained by the researchers, the generation of the models uses individuals performing of each activity to learn probabilities. This needed input signifies that, during the learning period, a log of the performed activity is recorded. Unfortunately, this information is in practice very difficult to obtain.

InTapia et al.(2004), the monitored patient indicates which activity he is performing.

Of course, the efficiency of this approach is confronted with the ability and the willingness of the person to declare his activity: in general, numerous reported activities errors are introduced in the database. In other works (Gaglio et al.,2015), experts are in charge of the enrichment of the database by studying sensor logs or by using cameras exclusively during the learning phase. This approach is expensive, intrusive and therefore risks changing the behaviour of the patient during the learning phase. In both cases, the labelling step is difficult and unreliable. That is the reason why, in the methods proposed in this thesis, the knowledge of actually performed activities during the learning phase is not required.