Collaborative signal processing for action recognition in body sensor networks

Ghasemzadeh, Hassan; Loseu, Vitali; Jafari, Roozbeh

doi:10.1145/1791212.1791242

Cited by 28 publications

(15 citation statements)

References 21 publications

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“…[Stiefmeier and Roggen 2007] has proposed a string matching technique for real-time gesture spotting. Finally, [Ghasemzadeh et al 2010] proposed a technique to construct motion transcript from inertial sensors and identify human movement by taking the collaboration between nodes into consideration. In contrast with the above mentioned approaches our goal in this paper is not automatic segmentation / classification of human movement, and we do not use inertial sensors.…”

Section: Related Workmentioning

confidence: 99%

Behavior-oriented data resource management in medical sensing systems

Noshadi

Dabiri

Meguerdichian

et al. 2013

ACM Trans. Sen. Netw.

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Wearable sensing systems have recently enabled a variety of medical monitoring and diagnostic applications in Wireless Health. The need for multiple sensors and constant monitoring leads these systems to be power hungry and expensive, with short operating lifetimes. We introduce a novel methodology that takes advantage of contextual and semantic properties in human behavior to enable efficient design and optimization of such systems from the data and information point of view. This, in turn, directly influences the wireless communication and local processing power consumption. We exploit intrinsic space and temporal correlations between sensor data while considering both user and system contextual behavior. Our goal is to select a small subset of sensors that accurately capture and/or predict all possible signals of a fully instrumented wearable sensing system. Our approach leverages novel modeling, partitioning, and behavioral optimization, which consists of signal characterization, segmentation and time shifting, mutual signal prediction, and a simultaneous minimization composed of subset sensor selection and opportunistic sampling. We demonstrate the effectiveness of the technique on an insole instrumented with 99 pressure sensors placed in each shoe, which cover the bottom of the entire foot, resulting in energy reduction of 72% to 97% for error rates of 5% to 17.5%.

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Section: Related Workmentioning

confidence: 99%

Behavior-oriented data resource management in medical sensing systems

Noshadi

Dabiri

Meguerdichian

et al. 2013

ACM Trans. Sen. Netw.

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“…An approach for structural data representation and recognition is proposed in [12]. This approach has a major weakness.…”

Section: Related Workmentioning

confidence: 99%

A mining technique using n-grams and motion transcripts for body sensor network data repository

Loseu

Ghasemzadeh

Khan

et al. 2010

Wireless Health 2010

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Recent years have seen a large influx of applications in the field of Body Sensor Networks (BSN). BSN, and in general wearable computers with sensors, can give researchers, users or clinicians access to tremendously valuable information extracted from data that were collected in users' natural environment. With this information, one can monitor the progression of a disease, identify its early onset or simply assess user's wellness. One major obstacle is managing repositories that store large amounts of BSN data. To address this issue, we propose a data mining approach for large BSN data repositories. We represent sensor readings with motion transcripts that maintain structural properties of the signal. To further take advantage of the signal's structure, we define a data mining technique using n-grams. We reduce overwhelmingly large number of n-grams via information gain (IG) feature selection. We report the effectiveness of our approach in terms of the speed of mining while maintaining an acceptable accuracy in terms of precision and recall. We demonstrate that the system can achieve average 99% precision with an average 100% recall on our pilot data with the help of only one transition for each movement.

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“…Consequently, the last decade has witnessed tremendous efforts in utilizing smart technologies such as BSNs for health monitoring and diagnosis through physical activity monitoring/assessment. Recent years have seen considerable research demonstrating the potential of BSNs in a variety of physical activity monitoring applications such as activity recognition [9,10,11,15,16,17], activity level estimation [18], caloric expenditure calculation [19,20], joint angle estimation [21], activity-based prompting [53,54,55,56,57,58], medication adherence assessment [59,60], crowd sensing [61,62,63,64,65,66], social networking [67,68,69,70], and sports training [22,23,24,25,26].…”

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confidence: 99%

“…Each segment has a multidimensional (feature) vector extracted from it, which will be used for classification [93,11]. The most widely used classification and event detection algorithms include k-NN (k-Nearest-Neighbor), Support Vector Machines (SVM), Hidden Markov Models (HMM), Neural Network (NN), Decision Tree Classifiers, Logistic Regression, and the Naive Bayesian approach [94,95,96,97,98,99].…”

mentioning

confidence: 99%