Activity Recognition from On-Body Sensors: Accuracy-Power Trade-Off by Dynamic Sensor Selection

Abstract-Activity recognition using onbody sensors are prone to degradation due to changes on sensor readings. The changes can occur because of degradation or alteration in the behavior of the sensor with respect to the others. In this paper we propose a method which detects anomalous nodes in the network and takes compensatory actions to keep the performance of the system as high as possible while the system is running. We show on two activity datasets with different configurations of onbody sensors that detection and compensation of anomalies make the system more robust against the changes.

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“…The first dataset (car manufacturing dataset) corresponds to a car manufacturing scenario [11], [12], as shown in Fig. 2.…”

Section: Methodsmentioning

confidence: 99%

Detecting and Rectifying Anomalies in Body Sensor Networks

Sagha

́n²,

Chavarriaga

2011

2011 International Conference on Body Sensor Networks

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“…Models describing statistical dependencies have also been used extensively, mainly in order to encode time-related correlations. One of the classical approaches, in this vein, are the Hidden Markov Models (HMMs) [16,35]. Authors in [32], propose a discriminative parameter learning method for a hybrid dynamic network in human activity recognition.…”

Section: Related Workmentioning

confidence: 99%

“…Following the above procedure, an overall accuracy equal to 98.8 % was achieved. Authors in [35] perform classification using Hidden Markov Models (HMM) on individual nodes. The resulting classifiers are fused by employing a Naive Bayes Classifier, achieving a total of 98 %.…”

Section: Skoda Mini Checkpoint Datasetmentioning

confidence: 99%

Landmark-based multimodal human action recognition

Asteriadis

Daras

2016

Multimed Tools Appl

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Human activity recognition has received a lot of attention recently, mainly thanks to the advancements in sensing technologies and systems' increasing computational power. However, complexity in human movements, sensing devices' noise and person-specific characteristics impose challenges that still remain to be overcome. In the proposed work, a novel, multi-modal human action recognition method is presented for handling the aforementioned issues. Each action is represented by a basis vector and spectral analysis is performed on an affinity matrix of new action feature vectors. Using modality-dependent kernel regressors for computing the affinity matrix, complexity is reduced and robust lowdimensional representations are achieved. The proposed scheme supports online adaptivity of modalities, in a dynamic fashion, according to their automatically inferred reliability. Evaluation on three publicly available datasets demonstrates the potential of the approach.

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“…There are several related studies which centre on the accuracy power trade off, for example in the proposals by Zappi et al (2008) and Wang et al (2009). Not only do these proposals operate with smartphones, but also with distributed body sensors, where it is possible to determine which sensors are the most important, and which can be disconnected in order to minimize energy cost.…”

Section: Embedded Sensors and Battery Impactmentioning

confidence: 99%

Discrete techniques applied to low-energy mobile human activity recognition. A new approach

Concepción

Morillo

Abril

et al. 2014

Expert Systems with Applications

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Human activity recognition systems are currently implemented by hundreds of applications and, in recent years, several technology manufacturers have introduced new wearable devices for this purpose. Battery consumption constitutes a critical point in these systems since most are provided with a rechargeable battery. In this paper, by using discrete techniques based on the Ameva algorithm, an innovative approach for human activity recognition systems on mobile devices is presented. Furthermore, unlike other systems in current use, this proposal enables recognition of high granularity activities by using accelerometer sensors. Hence, the accuracy of activity recognition systems can be increased without sacrificing efficiency. A comparative is carried out between the proposed approach and an approach based on the well-known neural networks.

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Activity Recognition from On-Body Sensors: Accuracy-Power Trade-Off by Dynamic Sensor Selection

Cited by 265 publications

References 25 publications

Detecting and Rectifying Anomalies in Body Sensor Networks

Detecting and Rectifying Anomalies in Body Sensor Networks

Landmark-based multimodal human action recognition

Discrete techniques applied to low-energy mobile human activity recognition. A new approach

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