Smart shoes design with embedded monitoring electronics system for healthcare and fitness applications

Hwang, Po-Yu; Chou, Chia-Ching; Fang, Wai-Chi; Hwang, Ching-Ming

doi:10.1109/icce-tw.2016.7520983

Cited by 7 publications

(2 citation statements)

References 2 publications

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“…In fact, they use accelerometers, gyroscope (Fan, 2015), and GPS (Gusenbauer, 2010) (Jain, 2017) (Lee, 2018) (Sukaphat, 2013) (Rundle, 2016) to obtain and analyze the required data. For example, Hwang et al have provided a fitness system embedded in shoes to measure steps, calorie, and biomedical information using the pressure sensors (Hwang, 2016).Adi et al have built a system to measure the heart rate, ECG signal, step count, and calorie consumption using the Smartphone's accelerometer (Pande, 2015). Pande et al used Smartphone sensors such as accelerometer and barometer to obtain energy expenditure (Kwapisz, 2011).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Motion Activity Recognition Using Smartphone Sensors

Gildeh¹,

Fotouhi²,

Fotouhi³

et al. 2020

Proceedings of the 12th International Conference on E-Health (EH2020)

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MHealth systems establish a new way to transfer the health service to remote places. These systems offer significant benefits for continuous health monitoring. Motion activity recognition is one of the challenging mHealth use cases that incorporates continuous data collection and analysis of measurements. The main goal of this research is to analyze physical activity data. We employ measurements from the WISDM lab dataset 1 . These data are collected from participants performing motion activities. This data is then used by deep learning algorithms to predict special activities. In particular, CNN and CNN-LSTM algorithms are used to compare their accuracy, which resulted in approximately 95% and 97% respectively. Thus, the CNN-LSTM has higher accuracy in this analysis.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Motion Activity Recognition Using Smartphone Sensors

Gildeh¹,

Fotouhi²,

Fotouhi³

et al. 2020

Proceedings of the 12th International Conference on E-Health (EH2020)

View full text Add to dashboard Cite

show abstract

“…The use of GPS coordinate tracking for the emergency response team will decrease the response time. 7,8 The eminence of wearable antennas has played a consequential part in the formation of a wireless-connection link between the material worn on the body and the encompassing matrix, which is both stable and effective. Due to the intensified concerns that have built-up in recent times pertaining to antennas and the generation of research in this particular field related to body communication systems, the IEEE 802.15 standardization group was formed.…”

mentioning

confidence: 99%

Electro-textile wearable antennas in wireless body area networks: materials, antenna design, manufacturing techniques, and human body consideration—a review

Almohammed

Ismail

2020

Textile Research Journal

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The latest progress in the emerging wireless technology has resulted in the development of wearable antennas made of various fabrics. This unique antenna is an integral part of the wireless body area network (WBAN). A wide range of applications are made by the wearable antennas in the fields of the Internet of Things, sport, defense, public safety, telemedicine, navigation, and tracking. The focus point of the current review is the recent progress of electro-textiles research with special attention on the materials properties, criteria, and manufacturing techniques. Existing as well as upcoming materials specifically and potentially used for electro-textile fabrication are discussed. Various manufacturing techniques are discussed to match the importance of material selection. The current work highlights the performance when the antennas tagging are applied in WBAN objects and illustrates how the human body affects the performance of the wearable antenna and vice versa. The findings of this review by introducing the best materials, techniques, and designs could be used in future to provide high-performance materials for body-centric applications.

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Intelligent Private Fitness System Based on ARM and Hybrid Internet of Things

Wang

Yuan

et al. 2017

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Smart shoes design with embedded monitoring electronics system for healthcare and fitness applications

Cited by 7 publications

References 2 publications

Deep Learning-Based Motion Activity Recognition Using Smartphone Sensors

Deep Learning-Based Motion Activity Recognition Using Smartphone Sensors

Electro-textile wearable antennas in wireless body area networks: materials, antenna design, manufacturing techniques, and human body consideration—a review

Intelligent Private Fitness System Based on ARM and Hybrid Internet of Things

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