Djamel Azzi

Real-time human ambulation, activity, and physiological monitoring: taxonomy of issues, techniques, applications, challenges and limitations.

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions.

Sensor data acquisition and processing parameters for human activity classification.

It is known that parameter selection for data sampling frequency and segmentation techniques (including different methods and window sizes) has an impact on the classification accuracy. For Ambient Assisted Living (AAL), no clear information to select these parameters exists, hence a wide variety and inconsistency across todays literature is observed. This paper presents the empirical investigation of different data sampling rates, segmentation techniques and segmentation window sizes and their effect on the accuracy of Activity of Daily Living (ADL) event classification and computational load for two different accelerometer sensor datasets. The study is conducted using an ANalysis Of VAriance (ANOVA) based on 32 different window sizes, three different segmentation algorithm (with and without overlap, totaling in six different parameters) and six sampling frequencies for nine common classification algorithms. The classification accuracy is based on a feature vector consisting of Root Mean Square (RMS), Mean, Signal Magnitude Area (SMA), Signal Vector Magnitude (here SMV), Energy, Entropy, FFTPeak, Standard Deviation (STD). The results are presented alongside recommendations for the parameter selection on the basis of the best performing parameter combinations that are identified by means of the corresponding Pareto curve.

A self‐healing mobile wireless sensor network using predictive reasoning

Purpose – The paper aims to investigate performance benefits associated with adopting a mobile wireless sensor network (WSN). Sensor nodes are generally energy constrained due to the latter being acquired from onboard battery cells. If one or more sensor nodes fail, possible coverage holes may be created which could invariantly lead to a reduced network lifetime. The paper proposes that instead of rendering the entire WSN inoperative, sensor nodes should physically change position within the region of interest thus adaptively altering the WSN topology with a view of recovering from failures. This type of motion will be referred to as “self healing”.Design/methodology/approach – This paper presents a mobility scheme based on Bayesian networks for predictive reasoning (BayesMob) which is essentially a distributed self healing algorithm for coordinating physical relocation of sensor nodes. Using the algorithm, sensor nodes can predict the performance of the WSN in terms of coverage given that the node moves ...

A Bayesian network approach to a biologically inspired motion strategy for mobile wireless sensor networks

Mobility strategies for wireless sensor networks (WSNs) are presented. We introduce a grazing mobility strategy for mobile WSNs, inspired by the foraging behaviour of herbivores grazing pastures. We present Bayesian network GRAZing (BNGRAZ) that implements the proposed WSN grazing strategy. BNGRAZ uses local neighbourhood information to predict coverage and connectivity performance changes related to sensor node motion characteristics. This enables a sensor node to predict the performance implications related to its direction of movement. We implement the BNGRAZ approach to grazing in a custom built mobile WSN simulator. The WSN performance criteria considered during the validation process include coverage, redundancy, connectivity, and network lifetime.

Artificial Immune Systems for Anomaly Detection in Ambient Assisted Living Applications

This paper makes a case for the use of Artificial Immune Systems AIS in the area of Ambient Assisted Living AAL for anomaly detection and long term monitoring. A brief literature review of some of the solutions developed for AAL and the use of AIS in other fields of research is presented. The authors advocate the use of AIS in AAL based on their unique features and their ability to address problems specific to the long term monitoring of people. An improved method for the optimisation of detector generation for AIS, which uses a novel intelligent seeding technique, is presented. The new seeding technique is compared with two other detector seeding methods. The simulation results are presented showing an improvement in the classification accuracy and warranting current and future work.

On time series sensor data segmentation for fall and activity classification

The vast amount of literature on human ambulation and Activities of Daily Living (ADL) events classification has highlighted significant details on most aspects of the research area including: monitoring techniques, Wearable Sensor-based Monitoring Device (WSMD) placement on human body parts, and ambulation and ADL data collection methods, among others. However literature has failed to highlight meaningful details on one of the most important aspects of such studies, sensor data segmentation for feature extraction. The choice of segmentation techniques is in general very important, because inappropriate segmentation will most likely result in features without discriminant power. No classifier of whatever sophistication will give meaningful results with features that have no discriminant power. The optimal segmentation technique has been empirically investigated using sensor data from a bi-axial accelerometer. Results of the empirical investigation are presented.

Telecare:legal, ethical and socioeconomic factors

Legal, ethical and socio-economic factors in community telecare differ from those pertaining to telemedicine and are examined with reference to older persons’ care. Issues discussed include equipment liability, service malpractice, technical and service standards, consent (including the Mental Capacity Act), research, trials, human factors, dependence, privacy, security, accessibility, quality, affordability, social inequalities and community factors.

Intelligent soft-computing based modelling of naturally ventilated buildings

The paper presents recent results on the application of the soft computing methodology for modelling of the internal climate in office buildings. More specifically, a part of a recently completed naturally ventilated building is considered which comprises three neighbouring offices and one corridor within the Portland Building at the University of Portsmouth. The approach adopted uses fuzzy logic for modelling, neural networks for adaptation and genetic algorithms for optimisation of the fuzzy model. The fuzzy models are of the Takagi-Sugeno type and are built by subtractive clustering. As a result of the latter, the initial values of the antecedent non-linear membership functions and the consequent linear algebraic equations parameters are determined. A method of extensive search of fuzzy model structures is presented which fully explores the dynamics of the plant. The model parameters are further adjusted by a back-propagation training neural network and a real-valued genetic algorithm in order to obtain a better fit to the measured data. Results with real data are presented for two types of models, namely Regression Delay and Proportional Difference. These models are applied for predicting internal air temperatures.

Activity Recognition from Video Data Using Spatial and Temporal Features

A method to monitor elderly people in an indoor environment using conventional cameras is presented. The method can be used to identify peoples activities and initiate suitable actions as needed. The originality of our approach is in combining spatial and temporal contexts with the position and orientation for the detected person. Preliminary evaluation, based only on the first two features (spatial and temporal), achieved the accuracy over 60% in a realistic residential environment. Although the results are based on using only two out of the four proposed input features, they already demonstrate a promising improvement over using a single feature in isolation.

Approaches to Bayesian Network Model Construction

Bayesian Network (BN) has sound mathematical basis, enables reasoning under uncertainty, and facilitates the update of beliefs, given new evidence. It also enables the visual representation of a model. These make BN suitable for solving uncertainty problems. This chapter details BN model construction approaches and presents our experiences with selecting the optimal construction approach.