An Assessment Method for Upper Limb Rehabilitation Training Using Kinect

Yang, Kaizhi; Peng, Liang; Tong, Lina; Liu, Rongkai; Liu, Bingyang

doi:10.1109/cyber.2018.8688256

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…Several studies have demonstrated that Kalman filter has achieved the best denoising performance when compared to other filter-based approaches [14]. Other types of filters such as double exponential smoothing filter [65], median filtering [67], fourth-order low-pass Butterworth filter [40]. also have been used.…”

Section: Related Workmentioning

confidence: 99%

Multiple Kinect based system to monitor and analyze key performance indicators of physical training

Ryselis

Petkus²,

Blažauskas

et al. 2020

Hum. Cent. Comput. Inf. Sci.

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Using a single Kinect device for human skeleton tracking and motion tracking lacks of reliability required in sports medicine and rehabilitation domains. Human joints reconstructed from non-standard poses such as squatting, sitting and lying are asymmetric and have unnatural lengths while their recognition error exceeds the error of recognizing standard poses. In order to achieve higher accuracy and usability for practical smart health applications we propose a practical solution for human skeleton tracking and analysis that performs the fusion of skeletal data from three Kinect devices to provide a complete 3D spatial coverage of a subject. The paper describes a novel data fusion algorithm using algebraic operations in vector space, the deployment of the system using three Kinect units, provides analysis of dynamic characteristics (position of joints, speed of movement, functional working envelope, body asymmetry and the rate of fatigue) of human motion during physical exercising, and evaluates intra-session reliability of the system using test–retest reliability metrics (intra-class correlation, coefficient of variation and coefficient of determination). Comparison of multi-Kinect system vs single-Kinect system shows an improvement in accuracy of 15.7%, while intra-session reliability is rated as excellent.

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Section: Related Workmentioning

confidence: 99%

Multiple Kinect based system to monitor and analyze key performance indicators of physical training

Ryselis

Petkus²,

Blažauskas

et al. 2020

Hum. Cent. Comput. Inf. Sci.

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show abstract

“…Thus, not all information obtained from sensors is relevant for subsequent use. It is almost always necessary to filter the input data to be satisfactory for the final product; for example, sensing people (namely for gaming using Kinect [9]), use in virtual reality, rehabilitation and similar situations from sensing people [10] to create 3D maps of the environment [11] requires specially designed filtering. It is possible to either filter static objects (the environment around people) or, on the contrary, filter known dynamic objects (e.g., a robotic arm).…”

Section: Introductionmentioning

confidence: 99%

“…Kinect [9]), use in virtual reality, rehabilitation and similar situations from sensing people [10] to create 3D maps of the environment [11] requires specially designed filtering. It is possible to either filter static objects (the environment around people) or, on the contrary, filter known dynamic objects (e.g., a robotic arm).…”

Section: Introductionmentioning

confidence: 99%

Distributed Camera Subsystem for Obstacle Detection

Oščádal

Spurný

Kot

et al. 2022

Sensors

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This work focuses on improving a camera system for sensing a workspace in which dynamic obstacles need to be detected. The currently available state-of-the-art solution (MoveIt!) processes data in a centralized manner from cameras that have to be registered before the system starts. Our solution enables distributed data processing and dynamic change in the number of sensors at runtime. The distributed camera data processing is implemented using a dedicated control unit on which the filtering is performed by comparing the real and expected depth images. Measurements of the processing speed of all sensor data into a global voxel map were compared between the centralized system (MoveIt!) and the new distributed system as part of a performance benchmark. The distributed system is more flexible in terms of sensitivity to a number of cameras, better framerate stability and the possibility of changing the camera number on the go. The effects of voxel grid size and camera resolution were also compared during the benchmark, where the distributed system showed better results. Finally, the overhead of data transmission in the network was discussed where the distributed system is considerably more efficient. The decentralized system proves to be faster by 38.7% with one camera and 71.5% with four cameras.

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The performance of Kinect in assessing the shoulder joint mobility

Paolis

Luca

2020

2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

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An Assessment Method for Upper Limb Rehabilitation Training Using Kinect

Cited by 3 publications

References 8 publications

Multiple Kinect based system to monitor and analyze key performance indicators of physical training

Multiple Kinect based system to monitor and analyze key performance indicators of physical training

Distributed Camera Subsystem for Obstacle Detection

The performance of Kinect in assessing the shoulder joint mobility

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