W8-Scope: Fine-Grained, Practical Monitoring of Weight Stack-based Exercises

Radhakrishnan, Meera; Misra, Archan; Balan, Rajesh Krishna

doi:10.1109/percom45495.2020.9127379

Cited by 2 publications

(3 citation statements)

References 28 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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“…In the following section we analyse previous and related work for runtime support of AI in pervasive computing. Today, AI is an important part of many pervasive computing approaches, either executed locally (Fellicious 2018;Fukushima et al 2018;Krupitzer et al 2018;Radhakrishnan et al 2020;Turky et al 2020;Zhang et al 2017;Zhou et al 2015) or in the Cloud (Clemente et al 2020;Muhammad et al 2019;Tantawi and Steinder 2019). However, these approaches focus on how to use AI in their respective application areas.…”

Section: Related Workmentioning

confidence: 99%

“…With them, pervasive systems can e.g. detect user behaviour (Clemente et al 2020;Radhakrishnan et al 2020;Zhou et al 2015), analyse physical environments (Fukushima et al 2018;Zhang et al 2017) and optimize system operation (Burger et al 2020b).…”

Section: Introductionmentioning

confidence: 99%

Elastic AI: system support for adaptive machine learning in pervasive computing systems

Cichiwskyj

Schmeißer

Qian

et al. 2021

CCF Trans. Pervasive Comp. Interact.

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Artificial intelligence (AI) is an important part of today’s pervasive computing systems. Still, there is no end-to-end system platform that allows to deploy, update, manage and execute AI models in pervasive systems. We propose such a system platform in this paper. Most importantly, we reuse concepts and techniques from twenty years of pervasive computing research on how to enable runtime adaptation and apply it to AI. This allows to specify adaptive AI models that are able to react to a multitude of dynamic changes, e.g. with respect to available devices, networking conditions, but also application requirements and sensor data sources. Developers can optimise their applications iteratively, starting with a generic setup and refining it step by step towards their specific pervasive computing scenario. To show the applicability of our platform, we apply it to two pervasive use cases and evaluate them, achieving up to four times faster inference and three times lower energy consumption compared to a classical AI deployment.

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W8-Scope: Fine-Grained, Practical Monitoring of Weight Stack-based Exercises

Cited by 2 publications

References 28 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

Elastic AI: system support for adaptive machine learning in pervasive computing systems

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