Deeper Exercise Monitoring for Smart Gym using Fused RFID and CV Data

Liu, Zijuan; Liu, Xiulong; Li, Keqiu

doi:10.1109/infocom41043.2020.9155360

Cited by 20 publications

(3 citation statements)

References 27 publications

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“…An implementation of a micro-watt-level power consumption is presented in [ 12 ], seven popular gym workouts were recognized and counting was achieved based on a body-capacitance-based sensor with three different body positions. An RFID and computer-vision-technique-based deeper gym exercise monitoring system (DEEM) was introduced in [ 13 ]. The DEEM system can determine the user and the objects that users hold.…”

Section: Related Workmentioning

confidence: 99%

Sensor-Based Gym Physical Exercise Recognition: Data Acquisition and Experiments

Hussain

Zafar

Baig

et al. 2022

Sensors

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Automatic tracking and quantification of exercises not only helps in motivating people but also contributes towards improving health conditions. Weight training, in addition to aerobic exercises, is an important component of a balanced exercise program. Excellent trackers are available for aerobic exercises but, in contrast, tracking free weight exercises is still performed manually. This study presents the details of our data acquisition effort using a single chest-mounted tri-axial accelerometer, followed by a novel method for the recognition of a wide range of gym-based free weight exercises. Exercises are recognized using LSTM neural networks and the reported results confirm the feasibility of the proposed approach. We train and test several LSTM-based gym exercise recognition models. More specifically, in one set of experiments, we experiment with separate models, one for each muscle group. In another experiment, we develop a universal model for all exercises. We believe that the promising results will potentially contribute to the vision of an automated system for comprehensive monitoring and analysis of gym-based exercises and create a new experience for exercising by freeing the exerciser from manual record-keeping.

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Section: Related Workmentioning

confidence: 99%

Sensor-Based Gym Physical Exercise Recognition: Data Acquisition and Experiments

Hussain

Zafar

Baig

et al. 2022

Sensors

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“…RFID technology has become a new choice in the field of HAR as its low cost, small form size, and convenient deployment [1]. It has been widely used in various scenarios such as patient health monitoring [2], motion guidance in gyms [3], and activity monitoring on market shelves [4].…”

Section: Introductionmentioning

confidence: 99%

DeepMultiple: A Deep Learning Model for RFID-based Multi-object Activity Recognition

Shen,

Yang,

Chen

et al. 2023

International Conferences on Software Engineering and Knowledge Engineering

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Wireless sensing techniques for Human Activity Recognition (HAR) have been widely studied in recent years. At present, the research on HAR based on Radio Frequency Identification (RFID) is changing from the tag attachment method to the tag non-attachment method. Affected by multipath, the current solutions in tag non-attachment scenarios mainly focus on singleobject activity recognition, which is not suitable for multi-object scenarios. To address these issues, we propose DeepMultiple, a novel tag non-attachment activity recognition model for multiobject. The model first preprocesses the raw signal with filter and phase calibration, then it applies dilated convolution in the frequency domain to extract multi-object activity features, finally ProbSparse is used to optimize the vanilla Transformer-based Encoder to enhance the activity recognition ability. We deployed a single reader and antenna for multi-object activity tracking during the experiments to reduce deployment difficulties. Extensive experimental results show that DeepMultiple can recognize ten types of multi-object activities with 98.12% precision under different challenging settings, which has excellent performance compared with several state-of-the-art methods.

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“…With the emergence of 5G technology and the expansion of human activities, new applications such as industrial automation [1] and real-time environmental monitoring [2] [3] appear in remote regions. However, due to expensive construction and maintenance costs, cellular base stations are usually sparsely deployed or unavailable in remote regions [4].…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning-based Task Offloading in Satellite-Terrestrial Edge Computing Networks

Zhu

Liu

et al. 2021

2021 IEEE Wireless Communications and Networking Conference (WCNC)

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In remote regions (e.g., mountain and desert), cellular networks are usually sparsely deployed or unavailable. With the appearance of new applications (e.g., industrial automation and environment monitoring) in remote regions, resourceconstrained terminals become unable to meet the latency requirements. Meanwhile, offloading tasks to urban terrestrial cloud (TC) via satellite link will lead to high delay. To tackle above issues, Satellite Edge Computing architecture is proposed, i.e., users can offload computing tasks to visible satellites for executing. However, existing works are usually limited to offload tasks in pure satellite networks, and make offloading decisions based on the predefined models of users. Besides, the runtime consumption of existing algorithms is rather high.In this paper, we study the task offloading problem in satelliteterrestrial edge computing networks, where tasks can be executed by satellite or urban TC. The proposed Deep Reinforcement learning-based Task Offloading (DRTO) algorithm can accelerate learning process by adjusting the number of candidate locations. In addition, offloading location and bandwidth allocation only depend on the current channel states. Simulation results show that DRTO achieves near-optimal offloading cost performance with much less runtime consumption, which is more suitable for satellite-terrestrial network with fast fading channel.

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Deeper Exercise Monitoring for Smart Gym using Fused RFID and CV Data

Cited by 20 publications

References 27 publications

Sensor-Based Gym Physical Exercise Recognition: Data Acquisition and Experiments

Sensor-Based Gym Physical Exercise Recognition: Data Acquisition and Experiments

DeepMultiple: A Deep Learning Model for RFID-based Multi-object Activity Recognition

Deep Reinforcement Learning-based Task Offloading in Satellite-Terrestrial Edge Computing Networks

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