Assessing Motion Style Errors in Ski Jumping Using Inertial Sensor Devices

Brock, Heike; Ohgi, Yuji

doi:10.1109/jsen.2017.2699162

Cited by 22 publications

(11 citation statements)

References 32 publications

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“…The innovative approach will be built on previous studies on anthropometry [12,13,45], 3D motion analysis [25,[46][47][48][49], sensing technology [21,[37][38][39][40]50], and artificial intelligence (AI) [44,[50][51][52][53][54]. This multidisciplinary approach provides a new route to develop a wearable-based method for data analysis and interpretation (motor-control depiction) as well as to distill and package new findings from various areas in realizing the real-time biomechanical feedback training in practice.…”

Section: A Novel Route In Developing Wearables For Human Motor-skill mentioning

confidence: 99%

“…Since the inputs of the limited IMUs cannot mechanically determine the twochain model, AI technologies are the alternative ways for the two-chain model quantification. Studies have shown that AI techniques have become a powerful tool for helping to solve many challenging problems in human motor-skill evaluations and analyses [44,52,53,67].…”

Section: Ai For Motor-control Quantificationmentioning

confidence: 99%

“…Among the AI technologies, deep learning is considered as a powerful tool that percolates through to all application areas of AI, such as image identification, speech recognition, natural language processing, and, indeed, biofeedback support [68][69][70]. The success of deep learning networks encourages their implementation in further applications for the enhancement of human physical activities [52,54,67]. Recently, Nature Neuroscience has published the latest developments in the area of markerless, video-based motion tracking, indicating that the power of deep learning will enable motion tracking to human-like accuracy [53].…”

Section: Ai For Motor-control Quantificationmentioning

confidence: 99%

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Challenges and Future of Wearable Technology in Human Motor-Skill Learning and Optimization

Shan¹

2020

Sports Science and Human Health - Different Approaches

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Learning how to move is a challenging task. Even the most basic motor skill of walking requires years to develop and can quickly deteriorate due to aging and sedentary lifestyles. More specialized skills such as ballet and acrobatic kicks in soccer require "talent" and years of extensive practice to fully master. These practices can easily cause injuries if conducted improperly. 3D motion capture technologies are currently the best way to acquire human motor skill in biomechanical feedback training. Owing to their tremendous promise for a plethora of applications, wearable technologies have garnered great interest in biofeedback training. Using wearable technology, some physical activity parameters can be tracked in real time and a noninvasive way to indicate the physical progress of a trainee. Yet, the application of biomechanical wearables in human motor-skill learning, training, and optimization is still in its infant phase due to the absence of a reliable method. This chapter elaborates challenges faced by developing wearable biomechanical feedback devices and forecasts potential breakthroughs in this area. The overarching goal is to foster interdisciplinary studies on wearable technology to improve how we move.

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Section: A Novel Route In Developing Wearables For Human Motor-skill mentioning

confidence: 99%

Section: Ai For Motor-control Quantificationmentioning

confidence: 99%

Section: Ai For Motor-control Quantificationmentioning

confidence: 99%

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Challenges and Future of Wearable Technology in Human Motor-Skill Learning and Optimization

Shan¹

2020

Sports Science and Human Health - Different Approaches

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“…Consequently, it can be cumbersome to determine those data properties that work best for a given task, and often specific domain knowledge is necessary to ensure meaningful data processing. Common feature extractors range from simple statistical or spectral descriptions as obtained using Wavelet and Fourier transformations to kinematic-induced features such as body pose and body joint position [5]. Using IMUs or other wearable measurement instrumentation, the latter can generally not be determined directly and need to be estimated by further processing functions such as the Kalman Filter.…”

Section: The Benefit Of Deep Learningmentioning

confidence: 99%

“…Here, the most common sensors are Inertial Measurement Units (IMUs). They were used in various applications like swimming [4], ski jumping [5], crosscountry skiing [6], trampolining [7] or table-tennis [8]. Within the last years, increasing attention is furthermore paid to less obtrusive bio-sensors woven into garment [9] or even directly implanted in the skin [10].…”

Section: Introductionmentioning

confidence: 99%

Deep learning—Accelerating Next Generation Performance Analysis Systems?

Brock

2018

The 12th Conference of the International Sports Engineering Association

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Deep neural network architectures show superior performance in recognition and prediction tasks of the image, speech and natural language domains. The success of such multilayered networks encourages their implementation in further application scenarios as the retrieval of relevant motion information for performance enhancement in sports. However, to date deep learning is only seldom applied to activity recognition problems of the human motion domain. Therefore, its use for sports data analysis might remain abstract to many practitioners. This paper provides a survey on recent works in the field of high-performance motion data and examines relevant technologies for subsequent deployment in real training systems. In particular, it discusses aspects of data acquisition, processing and network modeling. Analysis suggests the advantage of deep neural networks under difficult and noisy data conditions. However, further research is necessary to confirm the benefit of deep learning for next generation performance analysis systems.

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