In-Home Posture Evaluation and Visual Feedback Training to Improve Posture with a Kinect-Based System in Parkinson’s Disease

Okada, Yohei

doi:10.4172/2165-7025.1000232

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…The Kinect is also being used for the assess ment of postural control [30], gait [31], balance [3], static foot posture [32], body sway [33], and common motor movements [34], all with promising comparisons to three-dimensional gold standard systems. The use of the Kinect as a rehabilitation tool outside of the clinic and in the home [35][36][37] suggests the sensor's portability and supports the recommendation of previous research to incorporate assessment throughout the continuum of care [38].…”

Section: Microsoft Kinectsupporting

confidence: 62%

Reliability and validity of the Microsoft Kinect for assessment of manual wheelchair propulsion

Milgrom¹,

OTD²,

Foreman³

et al. 2016

J Rehabil Res Dev

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Abstract-Concurrent validity and test-retest reliability of the Microsoft Kinect in quantification of manual wheelchair pro pulsion were examined. Data were collected from five manual wheelchair users on a roller system. Three Kinect sensors were used to assess test-retest reliability with a still pose. Three sys tems were used to assess concurrent validity of the Kinect to measure propulsion kinematics (joint angles, push loop charac teristics): Kinect, Motion Analysis, and Dartfish ProSuite (Dart fish joint angles were limited to shoulder and elbow flexion). Intraclass correlation coefficients (ICCs) revealed good reliabil ity (0.87-0.99) between five of the six joint angles (neck flexion, shoulder flexion, shoulder abduction, elbow flexion, wrist flexion). ICCs suggested good concurrent validity for elbow flexion between the Kinect and Dartfish and between the Kinect and Motion Analysis. Good concurrent validity was revealed for maximum height, hand-axle relationship, and maximum area (0.92-0.95) between the Kinect and Dartfish and maxi mum height and hand-axle relationship (0.89-0.96) between the Kinect and Motion Analysis. Analysis of variance revealed sig nificant differences (p < 0.05) in maximum length between Dartfish (mean 58.76 cm) and the Kinect (40.16 cm). Results pose promising research and clinical implications for propul sion assessment and overuse injury prevention with the applica tion of current findings to future technology.

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Section: Microsoft Kinectsupporting

confidence: 62%

Reliability and validity of the Microsoft Kinect for assessment of manual wheelchair propulsion

Milgrom¹,

OTD²,

Foreman³

et al. 2016

J Rehabil Res Dev

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“…In this study, a Kinectcentered motion analysis device integrating a Kinect v2.0 depth camera (RGB 1920 × 1080 pixels @30fps, depth camera 512 × 424 pixels @30fps, 4-microphone linear phased array, Microsoft) and an independent computer that ran a data capture program was developed by iFLYTEK Suzhou Research Institute 27 . The participants were asked to stand directly in front of the Kinect camera (at a distance of 2 meters) 48 at ease for 5 s and then actively correct their abnormal posture for 5 s. After that, they were asked to turn left for 90°, relax and stand for 5 s, and then actively correct their abnormal posture for another 5 s. Close-fitting clothing were required and long hair should be tied up 27,49,50 . After the recording, the values of the six selected features F1, F2, F3, F4, F5, and F7 were automatically obtained by computer algorithms.…”

Section: Methods Participantsmentioning

confidence: 99%

A summary index derived from Kinect to evaluate postural abnormalities severity in Parkinson’s Disease patients

Hong

Zhang

et al. 2022

npj Parkinsons Dis.

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Postural abnormalities are common disabling motor complications affecting patients with Parkinson’s disease (PD). We proposed a summary index for postural abnormalities (IPA) based on Kinect depth camera and explored the clinical value of this indicator. Seventy individuals with PD and thirty age-matched healthy controls (HCs) were enrolled. All participants were tested using a Kinect-based system with IPA automatically obtained by algorithms. Significant correlations were detected between IPA and the Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) total score (rs = 0.369, p = 0.002), MDS-UPDRS-III total score (rs = 0.431, p < 0.001), MDS-UPDRS-III 3.13 score (rs = 0.573, p < 0.001), MDS-UPDRS-III-bradykinesia score (rs = 0.311, p = 0.010), the 39-item Parkinson’s Disease Questionnaire (PDQ-39) (rs = 0.272, p = 0.0027) and the Berg Balance Scale (BBS) score (rs = −0.350, p = 0.006). The optimal cut-off value of IPA for distinguishing PD from HCs was 12.96 with a sensitivity of 97.14%, specificity of 100.00%, area under the curve (AUC) of 0.999 (0.997–1.002, p < 0.001), and adjusted AUC of 0.998 (0.993–1.000, p < 0.001). The optimal cut-off value of IPA for distinguishing between PD with and without postural abnormalities was 20.14 with a sensitivity, specificity, AUC and adjusted AUC of 77.78%, 73.53%, 0.817 (0.720–0.914, p < 0.001), and 0.783 (0.631–0.900, p < 0.001), respectively. IPA was significantly correlated to the clinical manifestations of PD patients, and could reflect the global severity of postural abnormalities in PD with important value in distinguishing PD from HCs and distinguishing PD with postural abnormalities from those without.

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“…The authors concluded that after 14 sessions of balance training, the PD patients showed improved performance in activities of daily living [48]. Abnormal posture [35], [49] Training based on self-correcting one's posture with visual feedback and maintaining the posture while focusing attention on proprioceptive and somatosensory feedback might correct misperceptions of the vertical position and improve standing posture in PD patients [50]. Stretching can reduce the shortening of flexor muscles that contributes to the abnormally flexed posture and balance exercise (alone or combined with other training modalities) can improve postural control [51].…”

Section: Pd Target Symptoms Exergames-related Evidence Based On Sgsmentioning

confidence: 99%

Serious games as a means for holistically supporting Parkinson's Disease patients: The i-PROGNOSIS personalized game suite framework

Dias

Konstantinidis

Diniz

et al. 2017

2017 9th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games)

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Parkinson's Disease (PD) is a well-known progressive and neurodegenerative disorder, resulting in motor and non-motor symptoms that significantly reduce the quality of life. Motivated by the increased adoption of serious games in health care interventions sector in the last years, an unobtrusive approach is explored here, realised via the i-PROGNOSIS interventions platform (www.i-prognosis.eu), consisting of a game-based suite. In this way, targeting intelligent early detection and intervention in PD area, the Personalised Game Suite (PGS) design approach is presented, integrating different serious games in a unified platform. Overall, this comprehensive review gives an overview of studies examining motor and non-motor symptoms for PD along with the PGS design approach, in order to inform researchers and game designers for its consideration in the self-management of PD.

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In-Home Posture Evaluation and Visual Feedback Training to Improve Posture with a Kinect-Based System in Parkinson’s Disease

Cited by 6 publications

References 29 publications

Reliability and validity of the Microsoft Kinect for assessment of manual wheelchair propulsion

Reliability and validity of the Microsoft Kinect for assessment of manual wheelchair propulsion

A summary index derived from Kinect to evaluate postural abnormalities severity in Parkinson’s Disease patients

Serious games as a means for holistically supporting Parkinson's Disease patients: The i-PROGNOSIS personalized game suite framework

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