PROJECT TITLE :
Delay-Constrained Capacity of the IEEE 802.11 DCF in Wireless Multihop Networks
Gait analysis is a vital method to determine human motion. Recently, longitudinal gait analysis received abundant attention from the medical and healthcare domains. The challenge in studies over extended time periods is the battery life. Thanks to the continual sensing and computing, wearable gait devices cannot fulfill a full-day work schedule. In this paper, we gift an energy-economical adaptive sensing framework to deal with this downside. Through presampling for content understanding, a selective sensing and sparsity-primarily based signal reconstruction technique is proposed. In specific, we develop and implement the new sensing theme in a very good insole system to scale back the amount of samples, while still preserving the knowledge integrity of gait parameters. Experimental results show the effectiveness of our method in knowledge point reduction. Our proposed method improves the battery life to ten.forty seven h, while normalized mean sq. error is among 10p.c.
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