Upper limb motion tracking with the integration of IMU and Kinect

Tian, Yang; Meng, Xiaohong; Tao, Dapeng; Liu, Dongquan; Chen, Feng

doi:10.1016/j.neucom.2015.01.071

Cited by 99 publications

(56 citation statements)

References 20 publications

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“…Some previous works concentrated of determining joint positions through fusion between IMU and Kinect [32,35], while others were interested in some joint angles but did not compare estimated values to reference systems [36]. Our achieved angle estimation error can be compared to other works presented in Table 1 [33,34,37].…”

Section: Discussionmentioning

confidence: 87%

“…The study used 10 subjects to validate their method, but did not compare the joint positions with a reference system. Tian et al proposed an unscented Kalman filter for fusion between IMUs and the Kinect camera [36]. They compared the estimated joint positions with a reference system, but did not study the error of the elbow angle estimation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Multi-Sensor Fusion Scheme to Improve the Accuracy of Knee Flexion Kinematics for Functional Rehabilitation Movements

Tannous

Istrate

Benlarbi-Delaï

et al. 2016

Sensors

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Exergames have been proposed as a potential tool to improve the current practice of musculoskeletal rehabilitation. Inertial or optical motion capture sensors are commonly used to track the subject’s movements. However, the use of these motion capture tools suffers from the lack of accuracy in estimating joint angles, which could lead to wrong data interpretation. In this study, we proposed a real time quaternion-based fusion scheme, based on the extended Kalman filter, between inertial and visual motion capture sensors, to improve the estimation accuracy of joint angles. The fusion outcome was compared to angles measured using a goniometer. The fusion output shows a better estimation, when compared to inertial measurement units and Kinect outputs. We noted a smaller error (3.96°) compared to the one obtained using inertial sensors (5.04°). The proposed multi-sensor fusion system is therefore accurate enough to be applied, in future works, to our serious game for musculoskeletal rehabilitation.

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Section: Discussionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

A New Multi-Sensor Fusion Scheme to Improve the Accuracy of Knee Flexion Kinematics for Functional Rehabilitation Movements

Tannous

Istrate

Benlarbi-Delaï

et al. 2016

Sensors

View full text Add to dashboard Cite

show abstract

“…In [67] the authors propose a sensor fusion algorithm to combine the Kinect and a set of wearable IMUs showing how the combined result achieves higher accuracy than any of the two systems. Additionally, in [66] an integration of IMUs and Kinect for the tracking of upper limb motions is proposed, showing again improved results when compared to the two separate systems. The work in [68] compares the use of IMUs and a Kinect-based system (Reha@Home) for gait analysis.…”

Section: Inertial-based Motion Capturementioning

confidence: 99%

“…Another emerging approach is to combine the outputs of the two systems [66,67,101]. In [67] the authors propose a sensor fusion algorithm to combine the Kinect and a set of wearable IMUs showing how the combined result achieves higher accuracy than any of the two systems.…”

Section: Inertial-based Motion Capturementioning

confidence: 99%

Kinect and wearable inertial sensors for motor rehabilitation programs at home: state of the art and an experimental comparison

2020

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“…This video-based technology uses multi-cameras to track ~40 reflective markers (their weights are negligible) attached on the body surface. Technically, the tracking can be equivalently done by using 40 wearable IMUs, a sensing technology that measures linear and angular motion with a triad of gyroscopes and triad of accelerometers [21,[37][38][39][40]. As such, motion analysis could switch from labs (multi-camera environment) to the field (wearables) [4], i.e., quantification of limbs' coordination would be no more restricted to labs and become an effortless daily routine for researchers and practitioners.…”

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

confidence: 99%

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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Upper limb motion tracking with the integration of IMU and Kinect

Cited by 99 publications

References 20 publications

A New Multi-Sensor Fusion Scheme to Improve the Accuracy of Knee Flexion Kinematics for Functional Rehabilitation Movements

A New Multi-Sensor Fusion Scheme to Improve the Accuracy of Knee Flexion Kinematics for Functional Rehabilitation Movements

Kinect and wearable inertial sensors for motor rehabilitation programs at home: state of the art and an experimental comparison

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

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