Consumer Wi‐Fi device based action quality recognition: An illustrative example of seated dumbbell press action

Yu-xi, Qin; Su, Pan; Zhou, Weiwei; Pan, Duowei; Li, Zibo

doi:10.1049/cmu2.12093

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…The majority of deep learning studies and startups have targeted recreational gym-goers [145,850]. For example, algorithms and models have been developed for exercise recognition [401,587,646,651,747], automatic training diary logging [359,403,774], repetition counting [27,639], adherence monitoring [103], and real-time exercise technique evaluation and correction [117,133,254,349,371,372,589,607,610,845,900].…”

Section: Deep Learning Enabled Exercise and Strength Trainingmentioning

confidence: 99%

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

Teikari¹,

Pietrusz²

2021

Preprint

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In strength training, personalised strength training (autoregulation) approaches have been used to individualise exercise programs with monitoring an for dynamic adjustment based on their responses to training. While this transition from tradition-based training to evidence-based training framework has been an improvement in training practices, we argue that the future of strength training will also incorporate deep learning models powered by data. We refer to this data-driven framework as precision strength training inspired by the similar modeling frameworks used in precision medicine. In contrast to current personalised training in which the acquired athlete data is often subject to human expert decision-making, we are anticipating the rise of human-in-the-loop systems with an augmented coach who will be doing decisions collaboratively with the machine. Similar to other precision frameworks, such as precision health, we envision such a future to take decades to be realised and we focus here on practical short-term targets on a way to long-term realisation. In this chapter, we will review the measurement technology needed for continuous data acquisition from an individual during training/physical activity, how to acquire these datasets for the development of such systems and, how a proof-of-concept system could be developed for powerlifting training with applicability to general strength and conditioning (S&C) and physical rehabilitation purposes. Additionally, we will evaluate how the user experience (UX) of the system feedback and visualisation could be designed.

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Section: Deep Learning Enabled Exercise and Strength Trainingmentioning

confidence: 99%

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

Teikari¹,

Pietrusz²

2021

Preprint

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“…Human activities affect the propagation of Wi-Fi radio waves and thus affect the received signals as shown in Figure 1. This phenomenon makes it possible to recognize human activities according to Wi-Fi signal variations [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

HR‐HAR: A hierarchical relation representation for human activity recognition based on Wi‐Fi

Jiang

2022

IET Communications

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The Wi-Fi-based human activity recognition shows immense potential, as it is device-free, non-intrusive to privacy, and low-cost. However, current learning-based recognition methods mostly adopt the hybrid representation without distinguished contributions of features to different activities, which will be seriously affected by environment variations and interference of other persons, and costly to extend to new activities. Therefore, this paper proposes HR-HAR, a hierarchical relation representation for human activity recognition, to improve the performance, extensibility, and robustness by exploiting the hierarchical relation of features of activities. The hierarchical relation reflects the different contributions of features to recognize different activities and effectively distinguishes similar activities. It naturally leads to a layered structure that can be extended to new activities without retraining the entire model. With the layered structure, HR-HAR first detects the existence of other persons and then processes un-interfered scene and interfered scene signals with different methods, so it is robust to the interference. The experimental results on the public dataset with 95.6% accuracy and on the self-collected dataset with 95.4% accuracy for un-interfered scene and 95.0% for interfered scene indicate that HR-HAR is of reliable performance on human activity recognition and is robust to environmental changes and interference of other persons.

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Direction-agnostic gesture recognition system using commercial WiFi devices

Qin,

Sigg,

Pan

et al. 2024

Computer Communications

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Consumer Wi‐Fi device based action quality recognition: An illustrative example of seated dumbbell press action

Cited by 3 publications

References 37 publications

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

HR‐HAR: A hierarchical relation representation for human activity recognition based on Wi‐Fi

Direction-agnostic gesture recognition system using commercial WiFi devices

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