High-dimensional Time-series Gait Analysis using a Full-body Wireless Wearable Motion Sensing System and Convolutional Neural Network

Gresham, Brandon; Torres, Juan P.; Britton, Jonathan; Ma, Ziwei; Parada, Anita B.; Gutierrez, Michelle L.; Lawrence, Mark; Tang, Wei

doi:10.1109/biocas54905.2022.9948596

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…Monitoring the health of hospital and nursing home patients is one of the fields in which machine learning has been found to be increasingly useful. The AI models trained for these purposes are varied depending on the exact nature of the task they are created to accomplish [ 69 , 70 ]. Applications involving the monitoring of the status of specific organs of patients can rely on various different medical equipment as well as visual and thermal cameras, such as monitoring a patient’s heart rate or brain activity, which are achieved with electrocardiograms and electroencephalograms.…”

Section: Applicationsmentioning

confidence: 99%

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

Biglari

Tang

2023

Sensors

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Machine learning is an expanding field with an ever-increasing role in everyday life, with its utility in the industrial, agricultural, and medical sectors being undeniable. Recently, this utility has come in the form of machine learning implementation on embedded system devices. While there have been steady advances in the performance, memory, and power consumption of embedded devices, most machine learning algorithms still have a very high power consumption and computational demand, making the implementation of embedded machine learning somewhat difficult. However, different devices can be implemented for different applications based on their overall processing power and performance. This paper presents an overview of several different implementations of machine learning on embedded systems divided by their specific device, application, specific machine learning algorithm, and sensors. We will mainly focus on NVIDIA Jetson and Raspberry Pi devices with a few different less utilized embedded computers, as well as which of these devices were more commonly used for specific applications in different fields. We will also briefly analyze the specific ML models most commonly implemented on the devices and the specific sensors that were used to gather input from the field. All of the papers included in this review were selected using Google Scholar and published papers in the IEEExplore database. The selection criterion for these papers was the usage of embedded computing systems in either a theoretical study or practical implementation of machine learning models. The papers needed to have provided either one or, preferably, all of the following results in their studies—the overall accuracy of the models on the system, the overall power consumption of the embedded machine learning system, and the inference time of their models on the embedded system. Embedded machine learning is experiencing an explosion in both scale and scope, both due to advances in system performance and machine learning models, as well as greater affordability and accessibility of both. Improvements are noted in quality, power usage, and effectiveness.

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Section: Applicationsmentioning

confidence: 99%

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

Biglari

Tang

2023

Sensors

Self Cite

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Intelligent Wearable Systems: Opportunities and Challenges in Health and Sports

Yang,

Amin,

Shihada

2024

ACM Comput. Surv.

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Wearable devices or wearables, designed to be attached to the human body, can gather personalized real-time data and continuously monitor an individual’s health status and physiological disposition in a non-invasive manner. Intelligent wearables integrate advanced machine learning (ML) algorithms to process complex data patterns and provide accurate insights. As a result, intelligent wearables have emerged as a groundbreaking innovation in the fields of sports and health, introducing a new paradigm in kinematic analysis and patient data evaluation. For example, virtual coaches offer feedback on athletes’ performance, while virtual physicians assist in customizing medication for patients. This article provides an overview of various types of intelligent wearables and their applications in health and sports, categorizes ML algorithms, and introduces the wireless body area sensor network (WBASN) used for communication in wearable sensors. Additionally, we discuss potential challenges and development directions that could shape the future of intelligent wearables and propose effective solutions for their continued enhancement. This article offers valuable insights into the exciting potential of intelligent wearables to transform healthcare and sports.

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High-dimensional Time-series Gait Analysis using a Full-body Wireless Wearable Motion Sensing System and Convolutional Neural Network

Cited by 2 publications

References 19 publications

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

Intelligent Wearable Systems: Opportunities and Challenges in Health and Sports

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