Wearable Sensing
An energy harvester for a smart contact lens that monitors the glucose level of a user is developed and demonstrated. The energy harvester captures a smartphone’s 2G cellular emission, and rectifies it into DC power to operate on-lens microelectronics for glucose detection and wireless data transmission. The energy harvester can reach a maximum Ra- dio Frequency (RF) to Direct Current (DC) power conversion efficiency of 47%. An electrically realistic human eye model is designed and fabricated using 3D printing technologies to assist in various measurements of the proposed energy harvester.
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The dataset is an extensive collection of labeled high-frequency Wi-Fi Radio Signal Strength (RSS) measurements corresponding to multiple hand gestures made near a smartphone under different spatial and data traffic scenarios. We open source the software code and an Android app (Winiff) to create this dataset, which is available at Github (https://github.com/mohaseeb/wisture). The dataset is created using an artificial traffic induction (between the phone and the access point) approach to enable useful and meaningful RSS value
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Recognition of human activities is one of the most promising research areas in artificial intelligence. This has come along with the technological advancement in sensing technologies as well as the high demand for applications that are mobile, context-aware, and real-time. We have used a smart watch (Apple iWatch) to collect sensory data for 14 ADL activities (Activities of Daily Living).
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The TST FB4FD dataset contains data acquired through a pair of smart shoes. The smart shoes are specifically designed for fall detection purposes and are equipped respectively with 3 Force Sensing Resistors (FSRs) and an inertial unit. More specifically, the dataset consists of 32 different falls and 8 activities of daily living (ADLs) performed by 17 healthy subjects aged between 21 and 55 years, for a total of 544 falls and 136 ADLs sequences .
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