Activity recognition, as an important component of behavioral monitoring and intervention, has attracted enormous attention, especially in Mobile Cloud Computing (MCC) and Remote Health Monitoring (RHM) paradigms. While recently resource constrained wearable devices have been gaining popularity, their battery life is limited and constrained by the frequent wireless transmission of data to more computationally powerful back-ends. This paper proposes an ultra-low power activity recognition system using a novel adaptive compressed sensing technique that aims to minimize transmission costs. Coarse-grained on-body sensor localization and unsupervised clustering modules are devised to autonomously reconfigure the compressed sensing module for further power saving. We perform a thorough heuristic optimization using Grammatical Evolution (GE) to ensure minimal computation overhead of the proposed methodology. Our evaluation on a real-world dataset and a low power wearable sensing node demonstrates that our approach can reduce the energy consumption of the wireless data transmission up to 81.2% and 61.5%, with up to 60.6% and 35.0% overall power savings in comparison with baseline and a naive state-of-the-art approaches, respectively. These solutions lead to an average activity recognition accuracy of 89.0%-only 4.8% less than the baseline accuracy-while having a negligible energy overhead of on-node computation.
Ankle edema an important symptom for monitoring patients with chronic systematic diseases. It is an important indicator of onset or exacerbation of a variety of diseases that disturb cardiovascular, renal, or hepatic system such as heart, liver, and kidney failure, diabetes, etc. The current approaches toward edema assessment are conducted during clinical visits. In-clinic assessments, in addition to being burdensome and expensive, are sometimes not reliable and neglect important contextual factors such as patient's physical activity level and body posture. A novel wearable sensor, namely SmartSock, equipped with accelerometer and flexible stretch sensor embedded in clothing is presented. SmartSock is powered by advanced machine learning, signal processing, and correlation techniques to provide real-time, reliable, and context-rich information in remote settings. Our experiments on human subjects indicate high confidence in activity and posture recognition (with an accuracy of > 96%) as well as reliable edema quantification with intra-class correlation and Pearson correlation of 0.97.
Leg swelling produced by retention of fluid in leg tissues is known as peripheral edema, which is regarded as a symptom for various systematic diseases such as heart or kidney failure. In current clinical practice, edema is manually assessed by clinical experts. Such an assessment can often be inaccurate and unreliable especially if it is made by different operators at different times. Despite the importance of monitoring edema for the purpose of evaluating the course of disease or the effect of treatment, quantifying peripheral edema in a continuous and accurate fashion has remained a challenge. In this paper, we propose a wearable real-time platform (namely, Smart-Cuff), which integrates advanced technologies in sensing, computation, and signal processing and machine learning for continuous and real-time edema monitoring in remote and in-home settings. Given that peripheral edema is highly dependent on various contextual attributes such as body posture, we present an activitysensitive approach to discard erroneous or contextually invalid sensor data in order to meet the requirements of both energy efficiency and quality of information. Examination of our hardware prototype demonstrates the effectiveness of the proposed forcesensitive resistor-based edema sensor (with an R 2 of 0.97 for our regression model) as well as the activity monitoring mechanism (over 99% accuracy) that provide the means to perform reliable data sanity check on ankle circumference measurements in a continuous manner.
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