Calorie Map: An Activity Intensity Monitoring System Based on Wireless Signals

Chiang, Ting-Hui; Chuang, Yi-Ta; Ke, Chia-Liang; Chen, Ling-Jyh; Tseng, Yu–Chee

doi:10.1109/wcnc.2017.7925708

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…However, using this method requires the user to wear the phone on their arm or attach it to gym equipment, which places an additional burden on the user and allows the system to be used only in fixed situations. Calorie Map [16] utilizes wireless signals to infer calories burned while exercising and monitors activity intensity. However, this method requires the user to work out within the Wi-Fi signal coverage and the Wi-Fi signal can easily be interfered with.…”

Section: Research In Exercise Intensity Monitoringmentioning

confidence: 99%

“…Smart speaker-based acoustic sensing technology [14] turns smart speakers into active sonars for fitness monitoring, but it can only monitor in an indoor environment with a smart speaker and the users cannot change their positions while using it. The Wi-Fi-based approaches [15,16] cannot be performed outdoors where there is no Wi-Fi signal or a weak Wi-Fi signal, and the Wi-Fi signal is susceptible to interference from the surrounding environment. Furthermore, all of the systems/products mentioned above mainly focus on recognizing the type of exercise being performed or counting the number of exercise repetitions and do not have the capability to perform exercise intensity monitoring.…”

Section: Introductionmentioning

confidence: 99%

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Rope Jumping Strength Monitoring on Smart Devices via Passive Acoustic Sensing

Hou

Liu

2022

Sensors

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Rope jumping, as a fitness exercise recommended by many sports medicine practitioners, can improve cardiorespiratory capacity and physical coordination. Existing rope jump monitoring systems have limitations in terms of convenience, comfort, and exercise intensity evaluation. This paper presents a rope jump monitoring system using passive acoustic sensing. Our system exploits the off-the-shelf smartphone and headphones to capture the user’s rope-jumping sound and breathing sound after exercise. Given the captured acoustic data, the system uses a short-time energy-based approach and the high correlation between rope jumping cycles to detect the rope-jumping sound frames, then applies a dual-threshold endpoint detection algorithm to calculate the number of rope jumps. Finally, our system performs regression predictions of exercise intensity based on features extracted from the jumping speed and the mel spectrograms of the user’s breathing sound. The significant advantage of the system lies in the solution of the problem of poorly characterized mel spectrograms. We employ an attentive mechanism-based GAN to generate optimized breathing sound mel spectrograms and apply domain adversarial adaptive in the network to improve the migration capability of the system. Through extensive experiments, our system achieves (on average) 0.32 and 2.3% error rates for the rope jumping count and exercise intensity evaluation, respectively.

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Section: Research In Exercise Intensity Monitoringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rope Jumping Strength Monitoring on Smart Devices via Passive Acoustic Sensing

Hou

Liu

2022

Sensors

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“…Thus, wireless localization technology, especially indoor localization technology, has become the practical basis for extensive applications. For example, in the healthcare of a home or hospital for detecting the location and activities of the elderly people or patients; 2,3 in smart spaces such as airports, shopping centers, and tourist attractions for location-based services; 4,5 in case of fire for location detection of firefighters and survivors to realize emergency rescue; 6 for indoor environmental monitoring and building energy saving control; 7 as well as for detecting and tracking the location of terrorists in safety protection. 8 Various wireless localization technologies such as GPS, 9 infrared, 10 ultrasonic, 11 and radio frequency (RF) identification 12 have been developed to support the needs of the aforementioned scenarios.…”

Section: Introductionmentioning

confidence: 99%

Multi-targets device-free localization based on sparse coding in smart city

Zhao

Qin

Guo

et al. 2019

International Journal of Distributed Sensor Networks

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With the continuous expansion of the market of device-free localization in smart cities, the requirements of device-free localization technology are becoming higher and higher. The large amount of high-dimensional data generated by the existing device-free localization technology will improve the positioning accuracy as well as increase the positioning time and complexity. The positions required from single target to multi-targets become a further increasing difficulty for device-free localization. In order to satisfy the practical localizing application in smart city, an efficient multi-target device-free localization method is proposed based on a sparse coding model. To accelerate the positioning as well as improve the localization accuracy, a sparse coding-based iterative shrinkage threshold algorithm (SC-IA) is proposed and a subspace sparse coding-based iterative shrinkage threshold algorithm (SSC-IA) is presented for different practical application requirements. Experiments with practical dataset are performed for single-target and multi-targets localization, respectively. Compared with three typical machine learning algorithms: deep learning based on auto encoder, K-nearest neighbor, and orthogonal matching pursuit, experimental results show that the proposed sparse coding-based iterative shrinkage threshold algorithm and subspace sparse coding-based iterative shrinkage threshold algorithm can achieve high localization accuracy and low time cost simultaneously, so as to be more practical and applicable for the development of smart city.

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