Continuous home monitoring of Parkinson’s disease using inertial sensors: A systematic review

Parkinson’s disease (PD) is a progressive neurological disorder of the central nervous system that deteriorates motor functions, while it is also accompanied by a large diversity of non-motor symptoms such as cognitive impairment and mood changes, hallucinations, and sleep disturbance. Parkinsonism is evaluated during clinical examinations and appropriate medical treatments are directed towards alleviating symptoms. Tri-axial accelerometers, gyroscopes, and magnetometers could be adopted to support clinicians in the decision-making process by objectively quantifying the patient’s condition. In this context, at-home data collections aim to capture motor function during daily living and unobstructedly assess the patients’ status and the disease’s symptoms for prolonged time periods. This review aims to collate existing literature on PD monitoring using inertial sensors while it focuses on papers with at least one free-living data capture unsupervised either directly or via videotapes. Twenty-four papers were selected at the end of the process: fourteen investigated gait impairments, eight of which focused on walking, three on turning, two on falls, and one on physical activity; ten articles on the other hand examined symptoms, including bradykinesia, tremor, dyskinesia, and motor state fluctuations in the on/off phenomenon. In summary, inertial sensors are capable of gathering data over a long period of time and have the potential to facilitate the monitoring of people with Parkinson’s, providing relevant information about their motor status. Concerning gait impairments, kinematic parameters (such as duration of gait cycle, step length, and velocity) were typically used to discern PD from healthy subjects, whereas for symptoms’ assessment, researchers were capable of achieving accuracies of over 90% in a free-living environment. Further investigations should be focused on the development of ad-hoc hardware and software capable of providing real-time feedback to clinicians and patients. In addition, features such as the wearability of the system and user comfort, set-up process, and instructions for use, need to be strongly considered in the development of wearable sensors for PD monitoring.

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Mild Gait Impairment and Its Potential Diagnostic Value in Patients with Early-Stage Parkinson’s Disease

Behavioural Neurology ◽

10.1155/2021/6696454 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Zhuang Wu ◽

Xu Jiang ◽

Min Zhong ◽

Bo Shen ◽

Jun Zhu ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Early Stage ◽

Wearable Sensors ◽

Diagnostic Value ◽

Gait Pattern ◽

Heel Strike ◽

Phase Time ◽

Gait Parameters ◽

Gait Impairments

Background and Purpose. Patients with early-stage Parkinson’s disease (PD) have gait impairments, and gait parameters may act as diagnostic biomarkers. We aimed to (1) comprehensively quantify gait impairments in early-stage PD and (2) evaluate the diagnostic value of gait parameters for early-stage PD. Methods. 32 patients with early-stage PD and 30 healthy control subjects (HC) were enrolled. All participants completed the instrumented stand and walk test, and gait data was collected using wearable sensors. Results. We observed increased variability of stride length (SL) ( P < 0.001 ), stance phase time (StPT) ( P = 0.004 ), and swing phase time (SwPT) ( P = 0.011 ) in PD. There were decreased heel strike (HS) ( P = 0.001 ), range of motion of knee ( P = 0.036 ), and hip joints ( P < 0.001 ) in PD. In symmetry analysis, no difference was found in any of the assessed gait parameters between HC and PD. Only total steps ( AUC = 0.763 , P < 0.001 ), SL ( AUC = 0.701 , P = 0.007 ), SL variability ( AUC = 0.769 , P < 0.001 ), StPT variability ( AUC = 0.712 , P = 0.004 ), and SwPT variability ( AUC = 0.688 , P = 0.011 ) had potential diagnostic value. When these five gait parameters were combined, the predictive power was found to increase, with the highest AUC of 0.802 ( P < 0.001 ). Conclusions. Patients with early-stage PD presented increased variability but still symmetrical gait pattern. Some specific gait parameters can be applied to diagnose early-stage PD which may increase diagnosis accuracy. Our findings are helpful to improve patient’s quality of life.

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A Web-Based System for Home Monitoring of Patients With Parkinson's Disease Using Wearable Sensors

IEEE Transactions on Biomedical Engineering ◽

10.1109/tbme.2010.2090044 ◽

2011 ◽

Vol 58 (3) ◽

pp. 831-836 ◽

Cited By ~ 100

Author(s):

Bor-Rong Chen ◽

S Patel ◽

T Buckley ◽

R Rednic ◽

D J McClure ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Wearable Sensors ◽

Home Monitoring ◽

Web Based ◽

Web Based System

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Assessing Gait in Parkinson’s Disease Using Wearable Motion Sensors: A Systematic Review

Diseases ◽

10.3390/diseases7010018 ◽

2019 ◽

Vol 7 (1) ◽

pp. 18 ◽

Cited By ~ 29

Author(s):

Lorenzo Brognara ◽

Pierpaolo Palumbo ◽

Bernd Grimm ◽

Luca Palmerini

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Gait Analysis ◽

Inertial Sensors ◽

Neurodegenerative Disorder ◽

Wearable Sensors ◽

Motion Sensors ◽

Temporal Parameters ◽

Spatio Temporal ◽

Wearable Motion Sensors

: Parkinson’s disease (PD) is a progressive neurodegenerative disorder. Gait impairments are common among people with PD. Wearable sensor systems can be used for gait analysis by providing spatio-temporal parameters useful to investigate the progression of gait problems in Parkinson disease. However, various methods and tools with very high variability have been developed. The aim of this study is to review published articles of the last 10 years (from 2008 to 2018) concerning the application of wearable sensors to assess spatio-temporal parameters of gait in patients with PD. We focus on inertial sensors used for gait analysis in the clinical environment (i.e., we do not cover the use of inertial sensors to monitor walking or general activities at home, in unsupervised environments). Materials and Methods: Relevant articles were searched in the Medline database using Pubmed. Results and Discussion: Two hundred ninety-four articles were initially identified while searching the scientific literature regarding this topic. Thirty-six articles were selected and included in this review. Conclusion: Wearable motion sensors are useful, non-invasive, low-cost, and objective tools that are being extensively used to perform gait analysis on PD patients. Being able to diagnose and monitor the progression of PD patients makes wearable sensors very useful to evaluate clinical efficacy before and after therapeutic interventions. However, there is no uniformity in the use of wearable sensors in terms of: number of sensors, positioning, chosen parameters, and other characteristics. Future research should focus on standardizing the measurement setup and selecting which spatio-temporal parameters are the most informative to analyze gait in PD. These parameters should be provided as standard assessments in all studies to increase replicability and comparability of results.

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Mobility monitoring using smart technologies for Parkinson’s disease in free-living environment

Collegian Journal of the Royal College of Nursing Australia ◽

10.1016/j.colegn.2017.11.005 ◽

2018 ◽

Vol 25 (5) ◽

pp. 549-560 ◽

Cited By ~ 5

Author(s):

Heesook Son ◽

Won Seok Park ◽

Hyerang Kim

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Living Environment ◽

Free Living ◽

Smart Technologies

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Limb and trunk accelerometer data collected with wearable sensors from subjects with Parkinson’s disease

Scientific Data ◽

10.1038/s41597-021-00831-z ◽

2021 ◽

Vol 8 (1) ◽

Cited By ~ 1

Author(s):

Gloria Vergara-Diaz ◽

Jean-Francois Daneault ◽

Federico Parisi ◽

Chen Admati ◽

Christina Alfonso ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Symptom Severity ◽

Wearable Sensors ◽

Motor Fluctuations ◽

Sensor Data ◽

Accelerometer Data ◽

Motor Tasks ◽

Specific Symptom ◽

At Home

AbstractParkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms. Dyskinesia and motor fluctuations are complications of PD medications. An objective measure of on/off time with/without dyskinesia has been sought for some time because it would facilitate the titration of medications. The objective of the dataset herein presented is to assess if wearable sensor data can be used to generate accurate estimates of limb-specific symptom severity. Nineteen subjects with PD experiencing motor fluctuations were asked to wear a total of five wearable sensors on both forearms and shanks, as well as on the lower back. Accelerometer data was collected for four days, including two laboratory visits lasting 3 to 4 hours each while the remainder of the time was spent at home and in the community. During the laboratory visits, subjects performed a battery of motor tasks while clinicians rated limb-specific symptom severity. At home, subjects were instructed to use a smartphone app that guided the periodic performance of a set of motor tasks.

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Gait and Balance Changes with Investigational Peripheral Nerve Cell Therapy during Deep Brain Stimulation in People with Parkinson’s Disease

Brain Sciences ◽

10.3390/brainsci11040500 ◽

2021 ◽

Vol 11 (4) ◽

pp. 500

Author(s):

Geetanjali Gera ◽

Zain Guduru ◽

Tritia Yamasaki ◽

Julie A. Gurwell ◽

Monica J. Chau ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Deep Brain Stimulation ◽

Peripheral Nerve ◽

Brain Stimulation ◽

Step Length ◽

Gait Velocity ◽

Balance Performance ◽

Improved Performance ◽

Deep Brain

Background: The efficacy of deep brain stimulation (DBS) and dopaminergic therapy is known to decrease over time. Hence, a new investigational approach combines implanting autologous injury-activated peripheral nerve grafts (APNG) at the time of bilateral DBS surgery to the globus pallidus interna. Objectives: In a study where APNG was unilaterally implanted into the substantia nigra, we explored the effects on clinical gait and balance assessments over two years in 14 individuals with Parkinson’s disease. Methods: Computerized gait and balance evaluations were performed without medication, and stimulation was in the off state for at least 12 h to best assess the role of APNG implantation alone. We hypothesized that APNG might improve gait and balance deficits associated with PD. Results: While people with a degenerative movement disorder typically worsen with time, none of the gait parameters significantly changed across visits in this 24 month study. The postural stability item in the UPDRS did not worsen from baseline to the 24-month follow-up. However, we measured gait and balance improvements in the two most affected individuals, who had moderate PD. In these two individuals, we observed an increase in gait velocity and step length that persisted over 6 and 24 months. Conclusions: Participants did not show worsening of gait and balance performance in the off therapy state two years after surgery, while the two most severely affected participants showed improved performance. Further studies may better address the long-term maintanenace of these results.

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Will Artificial Intelligence Replace the Movement Disorders Specialist for Diagnosing and Managing Parkinson’s Disease?

Journal of Parkinson s Disease ◽

10.3233/jpd-212545 ◽

2021 ◽

pp. 1-6

Author(s):

Matt Landers ◽

Suchi Saria ◽

Alberto J. Espay

Keyword(s):

Artificial Intelligence ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Precision Medicine ◽

Movement Disorders ◽

Living Environment ◽

Patient Status ◽

Patient Symptom ◽

Molecular Therapies ◽

Improve Patient

The use of artificial intelligence (AI) to help diagnose and manage disease is of increasing interest to researchers and clinicians. Volumes of health data are generated from smartphones and ubiquitous inexpensive sensors. By using these data, AI can offer otherwise unobtainable insights about disease burden and patient status in a free-living environment. Moreover, from clinical datasets AI can improve patient symptom monitoring and global epidemiologic efforts. While these applications are exciting, it is necessary to examine both the utility and limitations of these novel analytic methods. The most promising uses of AI remain aspirational. For example, defining the molecular subtypes of Parkinson’s disease will be assisted by future applications of AI to relevant datasets. This will allow clinicians to match patients to molecular therapies and will thus help launch precision medicine. Until AI proves its potential in pushing the frontier of precision medicine, its utility will primarily remain in individualized monitoring, complementing but not replacing movement disorders specialists.

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