Power-Aware Activity Monitoring Using Distributed Wearable Sensors

Abstract

Monitoring human movements using wireless wearable sensors finds applications in a variety of domains including healthcare and wellness. In these systems, sensory devices are tightly integrated with the human body and infer status of the user through signal and information processing. Typically, highly accurate observations can be made at the cost of deploying a sufficiently large number of sensors, which in turn results in increased energy consumption of the system and reduced adherence to using the system. Therefore, optimizing power consumption of the system while maintaining acceptable accuracy plays a crucial role in realizing these stringent resource constraint systems. In this paper, we present an activity monitoring approach that minimizes power consumption of the system subject to a lower bound on the classification accuracy. The system utilizes computationally simple template-matching blocks that perform classifications on individual sensor nodes. The system further employs a boosting approach to enhance accuracy of the distributed classifier by selecting a subset of sensors optimized in terms of power consumption and capable of achieving a given lower bound accuracy criterion. A proof-of-concept evaluation with three participants performing 14 transitional actions was conducted, where collected signals were segmented and labeled manually for each action. The results indicated that the proposed approach provides more than a 65% reduction in the power consumption of the signal processing, while maintaining 80% sensitivity in classifying human movements.

Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges

“…In activity recognition, these class labels are the type of the activities such as walking and running, jumping, jacking, punching [123] and non-exercise activities like climbing up stairs, climbing down stairs, cit-ups, vacuuming, brushing teeth [129], sitting and standing [136,137], clapping, throwing, bending [123], computer work, moving box [130,131], writing on notepad, closing and opening the door [112], lying down, turning left and right [132], falling down [138]. Finally, these class labels are combined using different fusing techniques including classical inference (summation, majority voting [112,134], borda count, highest rank, logistic regression [93]), voting and ensemble [130,139], boosting [140], Bayesian inference [112], and DempsterShafar's method [123].…”

Intelligent Healthcare Systems Assisted by Data Analytics and Mobile Computing

“…With the development of wearable devices, the wearable computing power needs to be further improved to better assist in medical and healthcare. Relevant studies on, e.g., computing power, connection methods, and power consumption, have been conducted [42][43][44]. Mobile cloud computing, big data, and other technologies are also promoting the development of wearable computing toward providing better technical support to mobile healthcare.…”

A Wellness Mobile Application for Smart Health: Pilot Study Design and Results

“…Indeed, in the last few years, the diffusion of BSNs has increased enormously with the introduction, at a mass production level, of smart wearable devices (particularly smart watches and bracelets) that are able to capture certain parameters such as body acceleration, electrocardiogram (ECG), pulse rate, and bio-impedance readings [7,8]. …”