Evaluation of Parkinson’s Disease at Home: Predicting Tremor from Wearable Sensors

Heijmans, Margot; Habets, Jeroen; Kuijf, Mark L.; Kubben, Pieter L.

doi:10.1109/embc.2019.8857717

Cited by 25 publications

(29 citation statements)

References 16 publications

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“…Across the included studies, 3 different modalities of assessment devices were used, summarized in Multimedia Appendix 1. The most frequently used assessment was an external device specific to hand assessment, with the most common types being wrist-worn accelerometers [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37] and specialized keyboards [38][39][40][41][42][43][44][45][46][47]. These designated external devices allowed the collection of information on reaction time, finger tapping speed, and finger dexterity.…”

Section: Modalities Of Hand Function Assessmentmentioning

confidence: 99%

“…The sample sizes of studies varied between 1 (case study) [26] and 495 participants [66] in the experimental groups. Most studies (41/74, 55%) included control groups of healthy individuals or those with nonneurological conditions in determining the discriminant validity of the assessments (Multimedia Appendix 1).…”

Section: Target Populationmentioning

confidence: 99%

“…Quantifying tremors can help determine the efficacy of pharmacological and rehabilitative therapies. The studies that examined this domain were conducted in participants with PD [19][20][21]23,24,[26][27][28][29][30][32][33][34]36,37,46,50,55,60,61,63,64,67,68,[74][75][76]78,79,81,87]. Hoffman et al [68] found a 100% agreement of their visual examination of hand tremor at rest in their evaluation of telerehabilitation administration of the MDS-UPDRS assessment in comparison with in-clinic evaluation.…”

Section: Hand Tremormentioning

confidence: 99%

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Remote Assessments of Hand Function in Neurological Disorders: Systematic Review

Gopal¹,

Hsu²,

Allen³

et al. 2022

JMIR Rehabil Assist Technol

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Background Loss of fine motor skills is observed in many neurological diseases, and remote monitoring assessments can aid in early diagnosis and intervention. Hand function can be regularly assessed to monitor loss of fine motor skills in people with central nervous system disorders; however, there are challenges to in-clinic assessments. Remotely assessing hand function could facilitate monitoring and supporting of early diagnosis and intervention when warranted. Objective Remote assessments can facilitate the tracking of limitations, aiding in early diagnosis and intervention. This study aims to systematically review existing evidence regarding the remote assessment of hand function in populations with chronic neurological dysfunction. Methods PubMed and MEDLINE, CINAHL, Web of Science, and Embase were searched for studies that reported remote assessment of hand function (ie, outside of traditional in-person clinical settings) in adults with chronic central nervous system disorders. We excluded studies that included participants with orthopedic upper limb dysfunction or used tools for intervention and treatment. We extracted data on the evaluated hand function domains, validity and reliability, feasibility, and stage of development. Results In total, 74 studies met the inclusion criteria for Parkinson disease (n=57, 77% studies), stroke (n=9, 12%), multiple sclerosis (n=6, 8%), spinal cord injury (n=1, 1%), and amyotrophic lateral sclerosis (n=1, 1%). Three assessment modalities were identified: external device (eg, wrist-worn accelerometer), smartphone or tablet, and telerehabilitation. The feasibility and overall participant acceptability were high. The most common hand function domains assessed included finger tapping speed (fine motor control and rigidity), hand tremor (pharmacological and rehabilitation efficacy), and finger dexterity (manipulation of small objects required for daily tasks) and handwriting (coordination). Although validity and reliability data were heterogeneous across studies, statistically significant correlations with traditional in-clinic metrics were most commonly reported for telerehabilitation and smartphone or tablet apps. The most readily implementable assessments were smartphone or tablet-based. Conclusions The findings show that remote assessment of hand function is feasible in neurological disorders. Although varied, the assessments allow clinicians to objectively record performance in multiple hand function domains, improving the reliability of traditional in-clinic assessments. Remote assessments, particularly via telerehabilitation and smartphone- or tablet-based apps that align with in-clinic metrics, facilitate clinic to home transitions, have few barriers to implementation, and prompt remote identification and treatment of hand function impairments.

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Section: Modalities Of Hand Function Assessmentmentioning

confidence: 99%

Section: Target Populationmentioning

confidence: 99%

Section: Hand Tremormentioning

confidence: 99%

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Remote Assessments of Hand Function in Neurological Disorders: Systematic Review

Gopal¹,

Hsu²,

Allen³

et al. 2022

JMIR Rehabil Assist Technol

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“…Christian Herff et., al. [4], proposed the main cause of Parkinson's disease is affecting the deep brain in the major part. So, to predict the beta oscillation of the brain wave.…”

Section: Literature Surveymentioning

confidence: 99%

Machine Learning Algorithms for Detection of Parkinson’s Disease using Motor Symptoms: Speech and Tremor

Bala¹,

Anusuya²

2020

IJRTE

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Generally, the diseases are classified into communicable and non-communicable. The communicable disease is that, which can be spread easily from humans to humans while non-communicable disease does not spread. In this paper, we discuss about Parkinson's disease and its analysis using machine learning algorithms. The analysis of data is done using supervised machine learning approach. This paper concentrates and briefs about various supervised learning algorithms and its analysis.

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“…Their device was used to identify tremor events by computing statistical indexes that were representative of motion patterns. In addition to a wrist IMU sensor, Heijmans et al (33) used also a second IMU positioned on the chest, together with a questionnaire for annotating tremor events during the day. The annotated data were used to predict tremor severity.…”

Section: Devices For Monitoring Tremor and Efficacy Of Therapiesmentioning

confidence: 99%

Wearable Devices for Assessment of Tremor

Vescio¹,

Quattrone

Nisticò

et al. 2021

Front. Neurol.

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Tremor is an impairing symptom associated with several neurological diseases. Some of such diseases are neurodegenerative, and tremor characterization may be of help in differential diagnosis. To date, electromyography (EMG) is the gold standard for the analysis and diagnosis of tremors. In the last decade, however, several studies have been conducted for the validation of different techniques and new, non-invasive, portable, or even wearable devices have been recently proposed as complementary tools to EMG for a better characterization of tremors. Such devices have proven to be useful for monitoring the efficacy of therapies or even aiding in differential diagnosis. The aim of this review is to present systematically such new solutions, trying to highlight their potentialities and limitations, with a hint to future developments.

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Evaluation of Parkinson’s Disease at Home: Predicting Tremor from Wearable Sensors

Cited by 25 publications

References 16 publications

Remote Assessments of Hand Function in Neurological Disorders: Systematic Review

Remote Assessments of Hand Function in Neurological Disorders: Systematic Review

Machine Learning Algorithms for Detection of Parkinson’s Disease using Motor Symptoms: Speech and Tremor

Wearable Devices for Assessment of Tremor

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