<i>BodyScan</i>

Fang, Biyi; Lane, Nicholas D.; Zhang, Mi; Boran, Aidan; Kawsar, Fahim

doi:10.1145/2906388.2906411

Cited by 79 publications

(10 citation statements)

References 33 publications

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“…or in-air finger gestures (circle left, right-left, up-down, infinity, open-close, etc.) contained in signal sequences [14,19,20,23,24,40,44,[82][83][84][85][86]108]. In order to reduce the impact of environmental changes, the cutting modules extract features from the amplitude stream [44].…”

Section: Time-domain Thresholdmentioning

confidence: 99%

“…Time-domain Pros: relatively simple calculation; Cons: vulnerable to environmental changes and noise maximum, minimum, mean, standard deviation, kurtosis, skewness, variance, median and median absolute deviation, percentiles, root sum square, interquartile range [13,18,37,40,42,43,46,[48][49][50]62,72,73,75,76,[78][79][80][83][84][85][88][89][90]97,108,127,130], time lag [37], power decline ratio [37], amplitude sequence [20,54,74,91], phase sequence [39,51,107], CV [38] Frequency-domain Pros: capture the periodical characteristics of human motion; Cons: large amount of calculation FFT coefficient [12,83], dominant frequency [43], power spectrum density [88], spectral entropy [43,57,70,72,…”

Section: Category Pros and Cons Featuresmentioning

confidence: 99%

“…Template matching recognition is often training-free [10,19,20,39,55,74,91,93,94]. Training-once classification requires the valid features robust to the variations in the surrounding environment [12,13,32,37,40,42,44,45,[47][48][49]56,59,60,70,72,73,75,76,78,79,83,84,89,90,92,127,130]. Deep learning automatically extracts features, which often requires only one time of training [16,17,21,48,59,67,77,88,95,96,108].…”

Section: Activity Classificationmentioning

confidence: 99%

“…Pros: no training needed; Cons: accuracy depending on the specific template Euclidean distance [19,94], DTW [10,20,39,42,55,74,91,93,97], EMD [54,70,81,89], Jaccard coefficients [69] Machine Learning Pros: high efficiency and robustness; Cons: a lot of training data required KNN [18,40,46,48,51,52,59,82,99,124], SVM [12,13,32,37,40,42,44,45,[47][48][49]56,59,60,70,72,73,75,76,78,79,83,84,89,...…”

Section: Template Matchingmentioning

confidence: 99%

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Human Activity Sensing with Wireless Signals: A Survey

Liu

Teng

Hong

2020

Sensors

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Wireless networks have been widely deployed with a high demand for wireless data traffic. The ubiquitous availability of wireless signals brings new opportunities for non-intrusive human activity sensing. To enhance a thorough understanding of existing wireless sensing techniques and provide insights for future directions, this survey conducts a review of the existing research on human activity sensing with wireless signals. We review and compare existing research of wireless human activity sensing from seven perspectives, including the types of wireless signals, theoretical models, signal preprocessing techniques, activity segmentation, feature extraction, classification, and application. With the development and deployment of new wireless technology, there will be more sensing opportunities in human activities. Based on the analysis of existing research, the survey points out seven challenges on wireless human activity sensing research: robustness, non-coexistence of sensing and communications, privacy, multiple user activity sensing, limited sensing range, complex deep learning, and lack of standard datasets. Finally, this survey presents four possible future research trends, including new theoretical models, the coexistence of sensing and communications, awareness of sensing on receivers, and constructing open datasets to enable new wireless sensing opportunities on human activities.

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Section: Time-domain Thresholdmentioning

confidence: 99%

Section: Category Pros and Cons Featuresmentioning

confidence: 99%

Section: Activity Classificationmentioning

confidence: 99%

Section: Template Matchingmentioning

confidence: 99%

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Human Activity Sensing with Wireless Signals: A Survey

Liu

Teng

Hong

2020

Sensors

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“…Wearable platform such as ZOE [23] Mercury [27] and Opo [20] have been designed by the research community to address specific sensing problems, or to explore specific research areas (like BodyScan [16]).…”

Section: Related Workmentioning

confidence: 99%

Amulet

Hester

Peters

Yun

et al. 2016

Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems CD-ROM

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Wearable technology enables a range of exciting new applications in health, commerce, and beyond. For many important applications, wearables must have battery life measured in weeks or months, not hours and days as in most current devices. Our vision of wearable platforms aims for long battery life but with the flexibility and security to support multiple applications. To achieve long battery life with a workload comprising apps from multiple developers, these platforms must have robust mechanisms for app isolation and developer tools for optimizing resource usage.We introduce the Amulet Platform for constrained wearable devices, which includes an ultra-low-power hardware architecture and a companion software framework, including a highly efficient event-driven programming model, low-power operating system, and developer tools for profiling ultra-lowpower applications at compile time. We present the design and evaluation of our prototype Amulet hardware and software, and show how the framework enables developers to write energy-efficient applications. Our prototype has battery lifetime lasting weeks or even months, depending on the application, and our interactive resource-profiling tool predicts battery lifetime within 6-10% of the measured lifetime.

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