Detecting and Rectifying Anomalies in Body Sensor Networks

Sagha, Hesam; ́n, Jose del R. Mill; Chavarriaga, Ricardo

doi:10.1109/bsn.2011.34

Cited by 21 publications

(13 citation statements)

References 12 publications

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“…Such measure can be used to infer an online estimation of the system reliability, a critical point for systems that have to deal with dynamic changing environments [13,21]. For example, if a sensor is considered non reliable (e.g., when the estimated shift Θ exceeds a given threshold), compensatory actions can be taken, such as its removal from a sensor network [13,23,22]. Figure 10 shows how the mean and standard deviation of the estimated shift correlates with the change in performance with respect to the original location, for both sensor displacement and rotation.…”

Section: Discussionmentioning

confidence: 99%

Unsupervised adaptation for acceleration-based activity recognition: robustness to sensor displacement and rotation

Chavarriaga

Bayati

Millán

2011

Pers Ubiquit Comput

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A common assumption in activity recognition is that the system remains unchanged between its design and its posterior operation. However, many factors affect the data distribution between two different experimental sessions. One of these factors is the potential change in the sensor location (e.g. due to replacement or slippage) affecting the classification performance. Assuming that changes in the sensor placement mainly result in shifts in the feature distributions, we propose an unsupervised adaptive classifier that calibrates itself using an online version of expectationmaximisation. Tests using three activity recognition scenarios show that the proposed adaptive algorithm is robust against shift in the feature space due to sensor displacement and rotation. Moreover, since the method estimates the change in the feature distribution it can also be used to roughly evaluate the reliability of the system during online operation.

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Section: Discussionmentioning

confidence: 99%

Unsupervised adaptation for acceleration-based activity recognition: robustness to sensor displacement and rotation

Chavarriaga

Bayati

Millán

2011

Pers Ubiquit Comput

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“…Researchers also propose top down, application-level methods to detect sensor faults [19,28]. The principle is to look at how sensor failure affects reasoners.…”

Section: Detection Techniques In Smart Home Environmentsmentioning

confidence: 99%

Detecting abnormal events on binary sensors in smart home environments

Stevenson

Dobson

2016

Pervasive and Mobile Computing

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With a rising ageing population, smart home technologies have been demonstrated as a promising paradigm to enable technology-driven healthcare delivery. Smart home technologies, composed of advanced sensing, computing, and communication technologies, offer an unprecedented opportunity to keep track of behaviours and activities of the elderly and provide context-aware services that enable the elderly to remain active and independent in their own homes. However, experiments in developed prototypes demonstrate that abnormal sensor events hamper the correct identification of critical (and potentially life-threatening) situations, and that existing learning, estimation, and time-based approaches to situation recognition are inaccurate and inflexible when applied to multiple people sharing a living space. We propose a novel technique, called CLEAN, that integrates the semantics of sensor readings with statistical outlier detection. We evaluate the technique against four real-world datasets across different environments including the datasets with multiple residents. The results have shown that CLEAN can successfully detect sensor anomaly and improve activity recognition accuracies.

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“…In these systems, one inevitable anomaly is the rotation or sliding of the sensors. If these anomalies can be detected, it is easier to take an appropriate counter-action for the misbehaving sensor, without the need of reconfiguring the whole system (Sagha et al, 2011a;Chavarriaga et al, 2011). This desirable characteristic is the core feature of Opportunistic activity recognition systems where elements in the network may appear, disappear or change during life time .…”

Section: Introductionmentioning

confidence: 99%