GymCam

Khurana, Rushil; Ahuja, Karan; Yu, Zac; Mankoff, Jennifer; Harrison, Chris; Goel, Mayank

doi:10.1145/3287063

Cited by 60 publications

(15 citation statements)

References 36 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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“…Finally, to the best of our knowledge, only a few works combine three tasks together [19,20]. Both of them utilize off-the-shelf human pose estimators to calculate the joint coordinates.…”

Section: Repetitive Countingmentioning

confidence: 99%

“…The number of cycles is counted by detecting the peaks of the angle change curve. Khurana et al [20] extracted features from keypoints trajectory, and applied an off-the-shelf multilayer regressor to obtain the counting results.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Enabled Multitask System for Exercise Recognition and Counting

Wang

Laamarti

et al. 2021

MTI

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Exercise is a prevailing topic in modern society as more people are pursuing a healthy lifestyle. Physical activities provide significant benefits to human well-being from the inside out. Human pose estimation, action recognition and repetitive counting fields developed rapidly in the past several years. However, few works combined them together to assist people in exercise. In this paper, we propose a multitask system covering the three domains. Different from existing methods, heatmaps, which are the byproducts of 2D human pose estimation models, are adopted for exercise recognition and counting. Recent heatmap processing methods have been proven effective in extracting dynamic body pose information. Inspired by this, we propose a deep-learning multitask model of exercise recognition and repetition counting. To the best of our knowledge, this approach is attempted for the first time. To meet the needs of the multitask model, we create a new dataset Rep-Penn with action, counting and speed labels. Our multitask system can estimate human pose, identify physical activities and count repeated motions. We achieved 95.69% accuracy in exercise recognition on the Rep-Penn dataset. The multitask model also performed well in repetitive counting with 0.004 Mean Average Error (MAE) and 0.997 Off-By-One (OBO) accuracy on the Rep-Penn dataset. Compared with existing frameworks, our method obtained state-of-the-art results.

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“…Dynamic time-warping method (DTW) [12] is applied for trajectory comparison with pre-stored trajectories. Khurana et al [13] proposed GymCam, a camera-based approach in an unconstrained environment, such as in a gym, to recognize, track, and count various workout exercises. Their proposed system can correctly segment exercises from other non-exercises with an accuracy of 84.6 % by tracking only repetitive movements commonly occurring during exercises.…”

Section: Related Workmentioning

confidence: 99%

ExerTrack—Towards Smart Surfaces to Track Exercises

Jarms

Kirchbuchner

et al. 2020

Technologies

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The concept of the quantified self has gained popularity in recent years with the hype of miniaturized gadgets to monitor vital fitness levels. Smartwatches or smartphone apps and other fitness trackers are overwhelming the market. Most aerobic exercises such as walking, running, or cycling can be accurately recognized using wearable devices. However whole-body exercises such as push-ups, bridges, and sit-ups are performed on the ground and thus cannot be precisely recognized by wearing only one accelerometer. Thus, a floor-based approach is preferred for recognizing whole-body activities. Computer vision techniques on image data also report high recognition accuracy; however, the presence of a camera tends to raise privacy issues in public areas. Therefore, we focus on combining the advantages of ubiquitous proximity-sensing with non-optical sensors to preserve privacy in public areas and maintain low computation cost with a sparse sensor implementation. Our solution is the ExerTrack, an off-the-shelf sports mat equipped with eight sparsely distributed capacitive proximity sensors to recognize eight whole-body fitness exercises with a user-independent recognition accuracy of 93.5% and a user-dependent recognition accuracy of 95.1% based on a test study with 9 participants each performing 2 full sessions. We adopt a template-based approach to count repetitions and reach a user-independent counting accuracy of 93.6%. The final model can run on a Raspberry Pi 3 in real time. This work includes data-processing of our proposed system and model selection to improve the recognition accuracy and data augmentation technique to regularize the network.

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GymCam

Cited by 60 publications

References 36 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

Deep Learning-Enabled Multitask System for Exercise Recognition and Counting

ExerTrack—Towards Smart Surfaces to Track Exercises

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