Wearable and Non-Invasive Sensors for Rock Climbing Applications: Science-Based Training and Performance Optimization

Breen, Miyuki; Reed, Taylor; Nishitani, Yoshiko; Jones, M.; Breen, Hannah M.; Breen, Michael S.

doi:10.3390/s23115080

Cited by 5 publications

(3 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study also points out the challenge of tool development for teaching and training sport climbing. Likewise, an interesting classification of sensors used in climbing, both indoors and outdoors, is included in [3]. Several groups are presented there that allow comparison in terms of invasive and non-invasive sensors and practical benefits and limitations, in addition to performance metrics.…”

Section: Related Workmentioning

confidence: 99%

“…Among them is that it requires efficient movements in order to make proper use of body energy to reach the target on a route, either lateral or ascending [2]. It also requires the development of strength in small muscles, psychological stress management in the face of a potential fall, and a visual-motor ability to visualize and reach different holds on a random route when the route has not been predefined [3]. The different movements performed by a climber are framed within one of three clearly defined stages, referred to here as phases, which can be analyzed in relation to the climber's pose and the speed of the body joints involved in the action [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Climbing Technique Evaluation by Means of Skeleton Video Stream Analysis

Beltrán Beltrán,

Richter,

Köstermeyer

et al. 2023

Sensors

View full text Add to dashboard Cite

Due to the growing interest in climbing, increasing importance has been given to research in the field of non-invasive, camera-based motion analysis. While existing work uses invasive technologies such as wearables or modified walls and holds, or focuses on competitive sports, we for the first time present a system that uses video analysis to automatically recognize six movement errors that are typical for novices with limited climbing experience. Climbing a complete route consists of three repetitive climbing phases. Therefore, a characteristic joint arrangement may be detected as an error in a specific climbing phase, while this exact arrangement may not considered to be an error in another climbing phase. That is why we introduced a finite state machine to determine the current phase and to check for errors that commonly occur in the current phase. The transition between the phases depends on which joints are being used. To capture joint movements, we use a fourth-generation iPad Pro with LiDAR to record climbing sequences in which we convert the climber’s 2-D skeleton provided by the Vision framework from Apple into 3-D joints using the LiDAR depth information. Thereupon, we introduced a method that derives whether a joint moves or not, determining the current phase. Finally, the 3-D joints are analyzed with respect to defined characteristic joint arrangements to identify possible motion errors. To present the feedback to the climber, we imitate a virtual mentor by realizing an application on the iPad that creates an analysis immediately after the climber has finished the route by pointing out the detected errors and by giving suggestions for improvement. Quantitative tests with three experienced climbers that were able to climb reference routes without any errors and intentionally with errors resulted in precision–recall curves evaluating the error detection performance. The results demonstrate that while the number of false positives is still in an acceptable range, the number of detected errors is sufficient to provide climbing novices with adequate suggestions for improvement. Moreover, our study reveals limitations that mainly originate from incorrect joint localizations caused by the LiDAR sensor range. With human pose estimation becoming increasingly reliable and with the advance of sensor capabilities, these limitations will have a decreasing impact on our system performance.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Climbing Technique Evaluation by Means of Skeleton Video Stream Analysis

Beltrán Beltrán,

Richter,

Köstermeyer

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Gazing is a crucial form of human behavioral information that contains a wealth of psychological insights and is a vital clue for comprehending human intentions and emotions [1]. Gaze estimation by the human eye has a wide range of applications, including medical treatment [2,3], virtual reality [4], human-computer interaction [5][6][7][8], market research [9], and other fields. Gaze estimation research can be broadly categorized into three areas based on different scenarios and applications: gaze point prediction [10], gaze target prediction [11], and three-dimensional gaze estimation [12,13].…”

Section: Introductionmentioning

confidence: 99%

Gaze Estimation Based on Convolutional Structure and Sliding Window-Based Attention Mechanism

Chen

et al. 2023

Sensors

View full text Add to dashboard Cite

The direction of human gaze is an important indicator of human behavior, reflecting the level of attention and cognitive state towards various visual stimuli in the environment. Convolutional neural networks have achieved good performance in gaze estimation tasks, but their global modeling capability is limited, making it difficult to further improve prediction performance. In recent years, transformer models have been introduced for gaze estimation and have achieved state-of-the-art performance. However, their slicing-and-mapping mechanism for processing local image patches can compromise local spatial information. Moreover, the single down-sampling rate and fixed-size tokens are not suitable for multiscale feature learning in gaze estimation tasks. To overcome these limitations, this study introduces a Swin Transformer for gaze estimation and designs two network architectures: a pure Swin Transformer gaze estimation model (SwinT-GE) and a hybrid gaze estimation model that combines convolutional structures with SwinT-GE (Res-Swin-GE). SwinT-GE uses the tiny version of the Swin Transformer for gaze estimation. Res-Swin-GE replaces the slicing-and-mapping mechanism of SwinT-GE with convolutional structures. Experimental results demonstrate that Res-Swin-GE significantly outperforms SwinT-GE, exhibiting strong competitiveness on the MpiiFaceGaze dataset and achieving a 7.5% performance improvement over existing state-of-the-art methods on the Eyediap dataset.

show abstract