Test–retest reliability of Kinect’s measurements for the evaluation of upper body recovery of stroke patients

Mobini, Amir; Behzadipour, Saeed; Saadat, Mozafar

doi:10.1186/s12938-015-0070-0

Cited by 49 publications

(35 citation statements)

References 34 publications

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“…Feasibility was assessed by: A quality of movements’ total average score > 80% that was automatically calculated by the Jintronix software using its own algorithms (20) andAn appreciation total average score > 80% or > 2.4 points that was calculated by a 9 item feasibility questionnaire performed at the end of the intervention. In particular, the appreciation score for the single participant was calculated through the answer to the question: “How much did you like the Jintronix technology?” If the participant answered “a lot” the score was 3 points, “moderately” = 2 points, “a little” = 1 point and “I didn’t like it” = 0 points.…”

Section: Methodsmentioning

confidence: 99%

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A new adaptive home-based exercise technology among older adults living in nursing home: A pilot study on feasibility, acceptability and physical performance

Valiani

Lauzé

Martel

et al. 2017

The Journal of nutrition, health and aging

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Objectives To explore the feasibility and acceptability of a new home-based exercise technology among older adults and to evaluate its efficacy on physical performance measures. Design Longitudinal clinical trial. Setting Oak Hammock at the University of Florida, a nursing home located in Gainesville, Florida. Participants Twelve pre-disabled older adults (≥75 years) living in a nursing home with a Short Physical Performance Battery (SPPB) score between 6 and 9 and no diagnosis of dementia. Intervention Thirty minutes of light intensity exercise (aerobic, strength and balance) two times per week for four weeks using a home-based physical activity technology called Jintronix. Measurements Feasibility and acceptability were assessed through a 9-item self-administered questionnaire and by exploring the percentage of quality of movements and time performing exercise which was calculated automatically by Jintronix technology. Physical performance measures were assessed through the SPPB score at baseline, after 4 weeks of intervention and after 3 months from the completion of the intervention. Results Twelve older adults (80.5±4.2 years old) performed light intensity exercise with Jintronix for a total of 51.9±7.9 minutes per week. Participants reached 87% score of quality of movements in strength and balance exercises, a global appreciation score of 91.7% and a global difficulty score of 36%. Compared to baseline, there was a significant improvement in SPPB score at the end of the intervention and at 3 months following the completion of the exercise program (0.67±0.98 and 1.08±0.99 respectively, p-value <0.05). Conclusion Jintronix technology is feasible and acceptable among pre-disabled older adults without dementia living in nursing home and is beneficial in improving their physical performance.

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Section: Methodsmentioning

confidence: 99%

“…A quality of movements’ total average score > 80% that was automatically calculated by the Jintronix software using its own algorithms (20) and…”

Section: Methodsmentioning

confidence: 99%

A new adaptive home-based exercise technology among older adults living in nursing home: A pilot study on feasibility, acceptability and physical performance

Valiani

Lauzé

Martel

et al. 2017

The Journal of nutrition, health and aging

View full text Add to dashboard Cite

show abstract

“…Relying on review from articles, for rehabilitation, the age of the learners does not often fall into the typical age classification found in most education systems [3]. Systems which could deeply affect the life of adult stroke patients, has been developed by [14] and [15]. [12] is another study with rehabilitation goal that tested on 11 participants (40 to 75 years old).…”

Section: Target Of Researchmentioning

confidence: 99%

A Review of Kinect Computing Research in Education and Rehabilitation

Dehkordi¹,

Ismail²,

Diah³

2018

IJET

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Recently Kinect technology device and its benefit in treatment approach are rapidly growing domain of interestingness for researchers and educators. For years, the games and applications, except their entertaining aspect, have discovered an educational and rehabilitation character. In recent years, gesture-based devices have evolved and attracted the attention of researchers, and thus to Microsoft Kinect Xbox. Already, many institutions worldwide use the Kinect to the learning or rehabilitation process. This paper indicates a review of articles to provide a preliminary reconsideration on Microsoft's Kinect for the Xbox 360. The 16 researches were reviewed in this study are in the filed of education or rehabilitation implemented to users with special needs. The distribution and reviews in the study were research methods, domain of research, learning content, and intended settings of the kinect system. The outcome of this study is the possible uses and limitations of kinect for education and rehabilitation.

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“…The Kinect SDK v2.0 features skeletal tracking with 3D locations of 25 joints for each skeleton [12]. Kinect v2 has been employed in gait analysis [13][14][15], balance and postural assessment [16,17], foot position tracking [18], gait rehabilitation training [19,20], upper limb functional assessment or rehabilitation training [4,[21][22][23][24][25].Several studies have assessed the agreement between Kinect sensor and 3DMC. Kinect sensor demonstrated good reliability in assessing temporal-spatial parameters such as timing, velocity, or movement distance of functional tasks for both healthy subjects and people with physical disorders [4,13,21,22,26,27].…”

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confidence: 99%

Deep Learning-Based Upper Limb Functional Assessment Using a Single Kinect v2 Sensor

Cai

2020

Sensors

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We develop a deep learning refined kinematic model for accurately assessing upper limb joint angles using a single Kinect v2 sensor. We train a long short-term memory recurrent neural network using a supervised machine learning architecture to compensate for the systematic error of the Kinect kinematic model, taking a marker-based three-dimensional motion capture system (3DMC) as the golden standard. A series of upper limb functional task experiments were conducted, namely hand to the contralateral shoulder, hand to mouth or drinking, combing hair, and hand to back pocket. Our deep learning-based model significantly improves the performance of a single Kinect v2 sensor for all investigated upper limb joint angles across all functional tasks. Using a single Kinect v2 sensor, our deep learning-based model could measure shoulder and elbow flexion/extension waveforms with mean CMCs >0.93 for all tasks, shoulder adduction/abduction, and internal/external rotation waveforms with mean CMCs >0.8 for most of the tasks. The mean deviations of angles at the point of target achieved and range of motion are under 5° for all investigated joint angles during all functional tasks. Compared with the 3DMC, our presented system is easier to operate and needs less laboratory space.

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Test–retest reliability of Kinect’s measurements for the evaluation of upper body recovery of stroke patients

Cited by 49 publications

References 34 publications

A new adaptive home-based exercise technology among older adults living in nursing home: A pilot study on feasibility, acceptability and physical performance

A new adaptive home-based exercise technology among older adults living in nursing home: A pilot study on feasibility, acceptability and physical performance

A Review of Kinect Computing Research in Education and Rehabilitation

Deep Learning-Based Upper Limb Functional Assessment Using a Single Kinect v2 Sensor

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