A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Fault-Tolerant Activity Recognition <s> Smart Homes offer improved living conditions and levels of independence for the elderly population who require support with both physical and cognitive functions. Within these environments sensing technologies provide a key facility to monitor the behaviour of the person and their interactions with the living environment. In this paper we investigate the effects of sensor failures on the 'trust' of activity inference processing. We introduce a sensor evidence reasoning network which has been developed for ADL recognition along with the ability of handling uncertainty that may occur at a sensor level. Details of the initial experiments which have been conducted in the assessment of ADLs in a smart laboratory environment using this model are presented. Finally, we present the findings from the analysis on experimental and simulation data taking into consideration the impact of sensor failure on the overall stage of inference processing. <s> BIB001 </s> A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Fault-Tolerant Activity Recognition <s> Advances in technology have provided the ability to equip the home environment with a layer of technology to provide a truly 'Smart Home'. These homes offer improved living conditions and levels of independence for the population who require support with both physical and cognitive functions. At the core of the Smart Home is a collection of sensing technology which is used to monitor the behaviour of the inhabitant and their interactions with the environment. A variety of different sensors measuring light, sound, contact and motion provide sufficient multi-dimensional information about the inhabitant to support the inference of activity determination. A problem which impinges upon the success of any information analysis is the fact that sensors may not always provide reliable information due to either faults, operational tolerance levels or corrupted data. In this paper we address the fusion process of contextual information derived from uncertain sensor data. Based on a series of information handling techniques, most notably the Dempster-Shafer theory of evidence and the Equally Weighted Sum operator, evidential contextual information is represented, analysed and merged to achieve a consensus in automatically inferring activities of daily living for inhabitants in Smart Homes. Within the paper we introduce the framework within which uncertainty can be managed and demonstrate the effects that the number of sensors in conjunction with the reliability level of each sensor can have on the overall decision making process. <s> BIB002 </s> A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Fault-Tolerant Activity Recognition <s> Smart Homes offer improved living conditions and levels of independence for the elderly population who require support with both physical and cognitive functions. Sensor technology development and communication networking have been well explored within the area of smart living environments to meet the demands for ageing in place. In contrast, information management still faces a challenge to be practically sound. In our current research we deploy the Dempster-Shafer theory of evidence to represent and reason with uncertain sensor data along with revision and merging techniques to resolve inconsistencies among information from different sources. We present a general framework for sensor information fusion and knowledge revision/merging especially for monitoring activities of daily living in a smart home. <s> BIB003 </s> A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Fault-Tolerant Activity Recognition <s> This paper explores a sensor fusion method within Smart Homes to be used to monitor human activities in addition to managing uncertainty in sensor based readings. A case study has shown that the Dempster-Shafer theory of evidence can incorporate the uncertainty derived from the sensor errors and the sensor context and infer the activity. The results from this work show that this method can detect a toileting activity within a Smart Home environment with an accuracy of 69.4%. <s> BIB004 </s> A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Fault-Tolerant Activity Recognition <s> This paper explores a revised evidential lattice structure designed for the purposes of activity recognition within Smart Homes. The proposed structure consists of three layers, an object layer, a context layer and an activity layer. These layers can be used to combine the mass functions derived from sensors along with sensor context and can subsequently be used to infer activities. We present the details of configuring the activity recognition process and perform an analysis on the relationship between the number of sensors and the number of layers. We also present the details of an empirical study on two public data sets. The results from this work has demonstrated that the proposed method is capable of correctly detecting activities with a high degree of accuracy (84.27%) with a dataset from MIT [4] and 82.49% with a dataset from the University of Amsterdam[10]. <s> BIB005 </s> A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Fault-Tolerant Activity Recognition <s> This paper explores a sensor fusion method applied within smart homes used for the purposes of monitoring human activities in addition to managing uncertainty in sensor-based readings. A three-layer lattice structure has been proposed, which can be used to combine the mass functions derived from sensors along with sensor context. The proposed model can be used to infer activities. Following evaluation of the proposed methodology it has been demonstrated that the Dempster-Shafer theory of evidence can incorporate the uncertainty derived from the sensor errors and the sensor context and subsequently infer the activity using the proposed lattice structure. The results from this study show that this method can detect a toileting activity within a smart home environment with an accuracy of 88.2%. <s> BIB006 </s> A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Fault-Tolerant Activity Recognition <s> Inexpensive wireless sensing products are dramatically reducing the cost of in-home sensing. However, these sensors have been found to fail often and prohibitive maintenance costs may negate the cost benefits of inexpensive hardware and do-it-yourself installation. In this paper, we describe a new technique called SMART that uses application-level semantics to detect, assess, and adapt to sensor failures. SMART detects sensor failures at run-time by analyzing the relative behavior of multiple classifier instances trained to recognize the same set of activities based on different subsets of sensors. Once a failure is detected, SMART assesses its importance and adapts the classifier ensemble in attempt to avoid maintenance dispatch. Evaluation on three homes from two public datasets shows that SMART decreases the number of maintenance dispatches by 55% on average, identifies non-fail-stop failures at run-time with more than 85% accuracy, and improves the activity recognition accuracy under sensor failures by 15% on average. <s> BIB007 </s> A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Fault-Tolerant Activity Recognition <s> We address the problem of abnormal behaviour recognition of the inhabitant of a smart home in the presence of unreliable sensors. The corner stone of this work is a two-level architecture sensor fusion based on the Transferable Belief Model (TBM). The novelty of our work lies in the way we detect both unreliable sensors and abnormal behaviour within our architecture by using a temporal analysis of conflict resulting from the fusion of sensors. Detection of abnormal behaviour is based on a prediction/observation process and the influence of the faulty sources is discarded by discounting coefficients. Our architecture is tested in a real-life setting using three heterogeneous sensors enabling the detection of impossible transitions between three possible postures: Sitting, Standing and Lying. The impact of having a faulty sensor management is also tested in the real-life experiment for posture detection. <s> BIB008 </s> A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Fault-Tolerant Activity Recognition <s> Smart homes are equipped with a variety of sensors to monitor the human activities. The information gathered from the heterogeneous sensors may not be always reliable and have different degrees of uncertainly. One of the most important techniques have been proposed to deal with uncertainty is Dempster-Shafer Theory (DST). In this paper, aims to define more precise sensor reliability and decrease uncertainty in sensor data in the activity recognition process within smart home. In the proposed method, in training step some models are built for per activity according extracted features from training samples and then in the prediction step when a new signal sensor is observed, the features extracted from that signal and applied to models and a weight is calculated for that sensor. These weights are considered as sensor reliability and uses in the decision making process. <s> BIB009 </s> A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Fault-Tolerant Activity Recognition <s> IoT enabled smart environments are expected to proliferate significantly in the near future, particularly in the context of monitoring services for wellness living, patient healthcare and elderly care. Timely maintenance of failed sensors is of critical importance in such deployments to ensure minimal disruption to monitoring services. However, maintenance of large and geographically spread deployments can be a significant challenge. We present Idea that significantly increases the vtime-before-repair for a smart home deployment, thereby reducing the maintenance overhead. Specifically, our approach leverages the facts that (a) there is inherent sensor redundancy when combinations of sensors monitor activities of daily living (ADLs) in smart environments, and (b) the impact of each sensor failure depends on the activities being monitored and the functional redundancy afforded by rest of the heterogeneous sensors available for detecting the activities. Consequently, Idea identifies homes that need to be fixed based on expected degradation in ADL detection performance, and optimizes maintenance scheduling accordingly. We demonstrate that our approach leads to 3--40 times fewer maintenance personnel than a scheme in which failed sensors are fixed without considering their impact. <s> BIB010
In addition to the fault-tolerant activity recognition implemented by SMART system BIB007  and Idea system BIB010 , a framework of fault-tolerant activity recognition was addressed by Hong et al. BIB001 BIB002 BIB003 . First, the effect of sensor failures on the accuracy of activity recognition was investigated. Only binary sensors were considered for monitoring the ADL in smart homes. Sensor evidence reasoning network was designed based on activity hierarchy of ontology for activity recognition while tolerating uncertainty in the sensors' measurements. The discounting values depend on the manufacturer statics on the sensors. To validate the proposed approach, a case scenario was presented. In addition, sensors data recordings were collected from smart laboratory environment of a kitchen area for four weeks, and then, offline analysis was performed to verify the sensor data with video recordings. The sensor data was fed to the evidential reasoning network that is based on the Dempster-Shafer theory. The performance of activity recognition was assessed with respect to the number and combinations of sensor failures. Mckeever et al.  have extended the evidence of theory to incorporate temporal features and evaluated their proposed framework on Kasteren dataset [20] (house A). A limitation of the approach is that expert knowledge is needed for the sensor mass functions and sensor quality. Also, knowledge from users is used to get information about the temporal features of activities. Liao et al. BIB004 BIB005 BIB006 have proposed an activity recognition framework that deals with uncertainty in sensor measurements based on Dempster-Shafer theory of evidence while considering the effect of historical information and activity patterns. This is implemented through a framework with a lattice structure, which has a context layer that includes combinations of sensors derived from the historical data of inhabitant. Two types of uncertainty sources were considered; sensor hardware and context uncertainty due to the variability in human activities. A case study was presented in addition to applying the proposed approach to a publicly available dataset (Tapia dataset, subject 1) [25] collected from an apartment equipped with binary sensors. The performance was evaluated using precision, recall and F-measure metrics for activity recognition. A Weighted Dempster-Shafer theory was presented by Javadi, Moshiri and Yazdi BIB009 , where a weight for each sensor is assigned based on the historical data and activity patterns of the resident. In the training phase, 10 models are built for each sensor, and then in the testing phase, a weight for each sensor is calculated based on the membership degree of each sensor signal to the sensor's models. The proposed method is applied to a dataset (Tapia dataset, subject 1) [25] and evaluated through the accuracy detection rate of toileting activity. A drawback in the experiments is that, sensor faults were not injected to the dataset. Abnormal behaviour recognition in the presence of sensor failures/uncertainties was addressed by Marhic et al., it is based on the evidential approach using transferable belief model (TBM) BIB008 . It is assumed that there are three or more heterogeneous redundant sensors per each monitored activity. The system consists of Sensor FDI and the Abnormal behaviour detection modules. The Sensor FDI analyses the conflict between the heterogeneous redundant sensors using sensor fusion calculated by the Smet's operator and two experts. Abnormal behaviour is then detected by comparing the normal behaviour of inhabitant represented by the Markov chain model (MCM) and the detected/predicted behaviour within the TBM framework. Experiments were conducted on datasets collected from performing sitting, lying and standing activities with various single sensor failures, during which pressure sensor, omni-directional vision sensor and an accelerometer were used. The authors showed the ability of the system to detect abnormal behaviour in presence of sensor failures (unplugging sensor for a period of time) and highlighted some limitations that could be addressed in the future. Methods for fault tolerance in Ambient Assisted Living were suggested by Ahvar et al.  . Data from binary sensors, e.g., movement sensors, may be corrected using a model of the inhabitant behaviour. While fault tolerance for analog data from sensors, e.g., temperature sensors, may be implemented using sensor fusion. However, the system was not verified against faults in a case study.