Leap motion evaluation for assessment of upper limb motor skills in Parkinson's disease

Background: Parkinson’s disease is a neurodegenerative disorder that causes impaired motor functions. Virtual reality technology may be recommended to optimize motor learning in a safe environment. The objective of this paper was to evaluate the effects of a novel immersive virtual reality technology used for serious games (Oculus Rift 2 plus leap motion controller—OR2-LMC) for upper limb outcomes (muscle strength, coordination, speed of movements, fine and gross dexterity). Another objective was to obtain qualitative data for participants’ experiences related to the intervention. Methods: A mixed methods intervention (embedded) study was used, with a qualitative design after a technology intervention (quantitative design). The intervention and qualitative design followed international guidelines and were integrated into the method and reporting subheadings. Results: Significant improvements were observed in strength (p = 0.028), fine (p = 0.026 to 0.028) and gross coordination dexterity, and speed movements (p = 0.039) in the affected side, with excellent compliance (100%) and a high level of satisfaction (3.66 ± 0.18 points out of the maximum of 4). No adverse side effects were observed. Qualitative findings described patients’ perspectives regarding OR2-LMC treatment, facilitators and barriers for adherence, OR2-LMC applications, and treatment improvements. Conclusions: The intervention showed positive results for the upper limbs, with elements of discordance, expansion, and confirmation between qualitative and quantitative results.

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“…Few studies have been conducted concerning the use of technology to improve dexterity and coordination in UL rehabilitation [3,[55][56][57].…”

Section: Discussionmentioning

confidence: 99%

“…In this line, Butt et al [56] assessed motor symptoms (bradykinesia, frequency, speed and amplitude disturbances, and tremor) in PD patients using an LMC. Sixteen PD patients and 12 healthy subjects performed pronation and supination of the forearms, opening and closing of hands, thumb-forefinger tapping, and postural tremor using the LMC.…”

Section: Discussionmentioning

confidence: 99%

The Impact of a Novel Immersive Virtual Reality Technology Associated with Serious Games in Parkinson’s Disease Patients on Upper Limb Rehabilitation: A Mixed Methods Intervention Study

Sánchez-Herrera-Baeza

Cano‐de‐la‐Cuerda

Oña

et al. 2020

Sensors

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“…Given that upper limb movement disorders are only mildly responsive to dopaminergic treatment, patients will benefit from regular follow‐up with their therapists . This leads to high logistic costs and time demands for the health care system, added to a considerable cost to patients . Communication and information technologies offer new possibilities for clinical assessment.…”

Section: Introductionmentioning

confidence: 99%

Agreement Between Face‐to‐Face and Tele‐assessment of Upper Limb Functioning in Patients with Parkinson Disease

Cabrera‐Martos

Ortíz‐Rubio

Torres‐Sánchez

et al. 2019

PM&R

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Background: Upper limb disturbances are prevalent in patients with Parkinson disease (PD) and can limit their participation in activities of daily living. Tele-assessment of upper limb motor symptoms using an Internet application may be an alternative for addressing the growing demand for monitoring of disease progression. Objective: To evaluate the level of agreement between face-to-face and tele-assessment of patients with PD. Design: Reliability study. Setting: Parkinson's Disease Association. Participants: Twenty-one patients with PD from a local association participated in the study. Methods: Patients attended a session for clinical face-to-face and real-time online tele-assessment. Main Outcome Measurements: Upper limb measures of function (assessed with the Manual Ability Measure 16), dexterity (evaluated using the coin rotation task), motor speed (assessed by the finger tapping test), tremor (evaluated with the Fahn-Tolosa-Marin Tremor Rating Scale), and range of motion (using the Kinovea software) were recorded by two independent researchers. Results: All the outcome measures evaluated showed a good interrater, intraclass correlation coefficient (ρ > 0.75). In addition, most confidence intervals were narrow and excluded 0.8. The lowest reliability was obtained for elbow flexion of the most affected upper limb (ρ = 0.75; confidence interval 0.49-0.89) and the highest reliability for finger tapping tests (ρ = 1; 1, 1). Conclusions: This study demonstrates high interrater reliability of upper limb tele-assessment in patients with PD compared to a face-to-face assessment.

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“…As an alternative to wearable sensors, videobased methods have been used to investigate the PD motor symptoms. Butt et al [26], [27], for example, used the hand motion data collected using an RGB-D camera to distinguish PD patients from healthy individuals at an accuracy of up to 85% using SVM. Alternatively, a semi-supervised classification algorithm based on k-means and self-organizing tree map clustering was applied in [28], obtaining accuracies in the range of 42-99% for patients with different levels of dyskinesia severity.…”

Section: A Related Workmentioning

confidence: 99%

A Multi-Layer Gaussian Process for Motor Symptom Estimation in People With Parkinson's Disease

Lang

Pfister

Fröhner

et al. 2019

IEEE Trans. Biomed. Eng.

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The assessment of Parkinson's disease (PD) poses a significant challenge as it is influenced by various factors which lead to a complex and fluctuating symptom manifestation. Thus, a frequent and objective PD assessment is highly valuable for effective health management of people with Parkinson's disease (PwP). Here, we propose a method for monitoring PwP by stochastically modeling the relationships between their wrist movements during unscripted daily activities and corresponding annotations about clinical displays of movement abnormalities. We approach the estimation of PD motor signs by independently modeling and hierarchically stacking Gaussian process models for three classes of commonly observed movement abnormalities in PwP including tremor, (non-tremulous) bradykinesia, and (non-tremulous) dyskinesia. We use clinically adopted severity measures as annotations for training the models, thus allowing our multi-layer Gaussian process prediction models to estimate not only their presence but also their severities. The experimental validation of our approach demonstrates strong agreement of the model predictions with these PD annotations. Our results show the proposed method produces promising results in objective monitoring of movement abnormalities of PD in the presence of arbitrary and unknown voluntary motions, and makes an important step towards continuous monitoring of PD in the home environment.

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Leap motion evaluation for assessment of upper limb motor skills in Parkinson's disease

Cited by 61 publications

References 17 publications

The Impact of a Novel Immersive Virtual Reality Technology Associated with Serious Games in Parkinson’s Disease Patients on Upper Limb Rehabilitation: A Mixed Methods Intervention Study

The Impact of a Novel Immersive Virtual Reality Technology Associated with Serious Games in Parkinson’s Disease Patients on Upper Limb Rehabilitation: A Mixed Methods Intervention Study

Agreement Between Face‐to‐Face and Tele‐assessment of Upper Limb Functioning in Patients with Parkinson Disease

A Multi-Layer Gaussian Process for Motor Symptom Estimation in People With Parkinson's Disease

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