Submit to Sensors Review for Sensors Propose a Special Issue

Journal Browser

► Journal Browser

Application of Wearable Technology for Neurological Conditions

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 40735

Share This Special Issue

Special Issue Editors

Dr. Rosie Morris

E-Mail Website
Guest Editor

Sport, Exercise and Rehabilitation, Northumbria University, Newcastle NE1 8ST, UK
Interests: physiotherapy; neurological disorders; gait; balance; cognition

Dr. Silvia Del Din

E-Mail Website
Guest Editor

Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle NE4 5TG, UK
Interests: wearable technology; digital health; digital mobility outcomes; gait; Parkinson’s disease; signal processing

Special Issue Information

Dear colleagues,

Wearable technology is revolutionizing healthcare, providing a more tailored approach to both disease diagnosis and management. Within neurological conditions, wearable technology can provide a cost-effective tool throughout the disease course, including diagnosis and rehabilitation.

The uptake in the use of wearable technology by both patients and clinicians will have a huge impact on the future of healthcare. Wearable technology will help improve diagnosis, provide cost-effective and non-invasive assessment tools, increase specificity for diagnosis, monitor disease progression, and inform ongoing disease management.

This Special Issue focuses on the use of wearable technology within neurological conditions. We encourage the submission of papers that focus on the use of wearable technology for diagnosis, disease management, and outcome measures within populations suffering from Parkinson’s disease, dementia, multiple sclerosis, stroke, and spinal cord injury amongst others. We are also seeking submissions of manuscripts related to the keywords listed below.

Dr. Rosie Morris
Dr. Silvia Del Din
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Neurological disorders
Movement disorders
Health monitoring
Movement/mobility
Fall prevention
Sleep
Cognition
Personalized medicine
Remote monitoring
Rehabilitation
Disease management
Gait analysis
Balance
Sensors
Upper limb function
Posture
Physical activity
Digital outcomes/digital health
Functional tests
Motor symptoms
Cameras
Pressure insoles

Published Papers (13 papers)

Download All Papers

Research

Jump to: Other

13 pages, 1610 KiB

Open AccessArticle

Validation of Commercial Activity Trackers in Everyday Life of People with Parkinson’s Disease

by Pieter Ginis, Maaike Goris, An De Groef, Astrid Blondeel, Moran Gilat, Heleen Demeyer, Thierry Troosters and Alice Nieuwboer

Sensors 2023, 23(8), 4156; https://doi.org/10.3390/s23084156 - 21 Apr 2023

Cited by 3 | Viewed by 1574

Abstract

Maintaining physical activity is an important clinical goal for people with Parkinson’s disease (PwPD). We investigated the validity of two commercial activity trackers (ATs) to measure daily step counts. We compared a wrist- and a hip-worn commercial AT against the research-grade Dynaport Movemonitor (DAM) during 14 days of daily use. Criterion validity was assessed in 28 PwPD and 30 healthy controls (HCs) by a 2 × 3 ANOVA and intraclass correlation coefficients (ICC_2,1). The ability to measure daily step fluctuations compared to the DAM was studied by a 2 × 3 ANOVA and Kendall correlations. We also explored compliance and user-friendliness. Both the ATs and the DAM measured significantly fewer steps/day in PwPD compared to HCs (p < 0.01). Step counts derived from the ATs showed good to excellent agreement with the DAM in both groups (ICC_2,1 > 0.83). Daily fluctuations were detected adequately by the ATs, showing moderate associations with DAM-rankings. While compliance was high overall, 22% of PwPD were disinclined to use the ATs after the study. Overall, we conclude that the ATs had sufficient agreement with the DAM for the purpose of promoting physical activity in mildly affected PwPD. However, further validation is needed before clinical use can be widely recommended. Full article

(This article belongs to the Special Issue Application of Wearable Technology for Neurological Conditions)

► Show Figures

Figure 1

17 pages, 4331 KiB

Open AccessArticle

The Influence of Stride Selection on Gait Parameters Collected with Inertial Sensors

by Carmen J. Ensink, Katrijn Smulders, Jolien J. E. Warnar and Noël L. W. Keijsers

Sensors 2023, 23(4), 2002; https://doi.org/10.3390/s23042002 - 10 Feb 2023

Cited by 1 | Viewed by 1734

Abstract

Different methods exist to select strides that represent preferred, steady-state gait. The aim of this study was to identify the effect of different stride-selection methods on spatiotemporal gait parameters to analyze steady-state gait. A total of 191 patients with hip or knee osteoarthritis (aged 38–85) wearing inertial sensors walked back and forth over 10 m for two minutes. After the removal of strides in turns, five stride-selection methods were compared: (ALL) include all strides, others removed (REFERENCE) two strides around turns, (ONE) one stride around turns, (LENGTH) strides <63% of median stride length, and (SPEED) strides that fall outside the 95% confidence interval of gait speed over the strides included in REFERENCE. Means and SDs of gait parameters were compared for each trial against the most conservative definition (REFERENCE). ONE and SPEED definitions resulted in similar means and SDs compared to REFERENCE, while ALL and LENGTH definitions resulted in substantially higher SDs of all gait parameters. An in-depth analysis of individual strides showed that the first two strides after and last two strides before a turn were significantly different from steady-state walking. Therefore, it is suggested to exclude the first two strides around turns to assess steady-state gait. Full article

(This article belongs to the Special Issue Application of Wearable Technology for Neurological Conditions)

► Show Figures

Figure 1

10 pages, 826 KiB

Open AccessArticle

Visual Cues for Turning in Parkinson’s Disease

by Julia Das, Rodrigo Vitorio, Allissa Butterfield, Rosie Morris, Lisa Graham, Gill Barry, Claire McDonald, Richard Walker, Martina Mancini and Samuel Stuart

Sensors 2022, 22(18), 6746; https://doi.org/10.3390/s22186746 - 7 Sep 2022

Cited by 3 | Viewed by 2274

Abstract

Turning is a common impairment of mobility in people with Parkinson’s disease (PD), which increases freezing of gait (FoG) episodes and has implications for falls risk. Visual cues have been shown to improve general gait characteristics in PD. However, the effects of visual cues on turning deficits in PD remains unclear. We aimed to (i) compare the response of turning performance while walking (180° and 360° turns) to visual cues in people with PD with and without FoG; and (ii) examine the relationship between FoG severity and response to visual cues during turning. This exploratory interventional study measured turning while walking in 43 participants with PD (22 with self-reported FoG) and 20 controls using an inertial sensor placed at the fifth lumbar vertebrae region. Participants walked straight and performed 180° and 360° turns midway through a 10 m walk, which was done with and without visual cues (starred pattern). The turn duration and velocity response to visual cues were assessed using linear mixed effects models. People with FoG turned slower and longer than people with PD without FoG and controls (group effect: p < 0.001). Visual cues reduced the velocity of turning 180° across all groups and reduced the velocity of turning 360° in people with PD without FoG and controls. FoG severity was not significantly associated with response to visual cues during turning. Findings suggest that visual cueing can modify turning during walking in PD, with response influenced by FoG status and turn amplitude. Slower turning in response to visual cueing may indicate a more cautious and/or attention-driven turning pattern. This study contributes to our understanding of the influence that cues can have on turning performance in PD, particularly in freezers, and will aid in their therapeutic application. Full article

(This article belongs to the Special Issue Application of Wearable Technology for Neurological Conditions)

► Show Figures

Figure 1

10 pages, 1069 KiB

Open AccessArticle

Fall Prediction Based on Instrumented Measures of Gait and Turning in Daily Life in People with Multiple Sclerosis

by Ishu Arpan, Vrutangkumar V. Shah, James McNames, Graham Harker, Patricia Carlson-Kuhta, Rebecca Spain, Mahmoud El-Gohary, Martina Mancini and Fay B. Horak

Sensors 2022, 22(16), 5940; https://doi.org/10.3390/s22165940 - 9 Aug 2022

Cited by 6 | Viewed by 2419

Abstract

This study investigates the potential of passive monitoring of gait and turning in daily life in people with multiple sclerosis (PwMS) to identify those at future risk of falls. Seven days of passive monitoring of gait and turning were carried out in a pilot study of 26 PwMS in home settings using wearable inertial sensors. The retrospective fall history was collected at the baseline. After gait and turning data collection in daily life, PwMS were followed biweekly for a year and were classified as fallers if they experienced >1 fall. The ability of short-term passive monitoring of gait and turning, as well as retrospective fall history to predict future falls were compared using receiver operator curves and regression analysis. The history of retrospective falls was not identified as a significant predictor of future falls in this cohort (AUC = 0.62, p = 0.32). Among quantitative monitoring measures of gait and turning, the pitch at toe-off was the best predictor of falls (AUC = 0.86, p < 0.01). Fallers had a smaller pitch of their feet at toe-off, reflecting less plantarflexion during the push-off phase of walking, which can impact forward propulsion and swing initiation and can result in poor foot clearance and an increased metabolic cost of walking. In conclusion, our cohort of PwMS showed that objective monitoring of gait and turning in daily life can identify those at future risk of falls, and the pitch at toe-off was the single most influential predictor of future falls. Therefore, interventions aimed at improving the strength of plantarflexion muscles, range of motion, and increased proprioceptive input may benefit PwMS at future fall risk. Full article

(This article belongs to the Special Issue Application of Wearable Technology for Neurological Conditions)

► Show Figures

Figure 1

13 pages, 317 KiB

Open AccessArticle

Personalised Gait Recognition for People with Neurological Conditions

by Leon Ingelse, Diogo Branco, Hristijan Gjoreski, Tiago Guerreiro, Raquel Bouça-Machado, Joaquim J. Ferreira and The CNS Physiotherapy Study Group

Sensors 2022, 22(11), 3980; https://doi.org/10.3390/s22113980 - 24 May 2022

Cited by 1 | Viewed by 2327

Abstract

There is growing interest in monitoring gait patterns in people with neurological conditions. The democratisation of wearable inertial sensors has enabled the study of gait in free living environments. One pivotal aspect of gait assessment in uncontrolled environments is the ability to accurately recognise gait instances. Previous work has focused on wavelet transform methods or general machine learning models to detect gait; the former assume a comparable gait pattern between people and the latter assume training datasets that represent a diverse population. In this paper, we argue that these approaches are unsuitable for people with severe motor impairments and their distinct gait patterns, and make the case for a lightweight personalised alternative. We propose an approach that builds on top of a general model, fine-tuning it with personalised data. A comparative proof-of-concept evaluation with general machine learning (NN and CNN) approaches and personalised counterparts showed that the latter improved the overall accuracy in 3.5% for the NN and 5.3% for the CNN. More importantly, participants that were ill-represented by the general model (the most extreme cases) had the recognition of gait instances improved by up to 16.9% for NN and 20.5% for CNN with the personalised approaches. It is common to say that people with neurological conditions, such as Parkinson’s disease, present very individual motor patterns, and that in a sense they are all outliers; we expect that our results will motivate researchers to explore alternative approaches that value personalisation rather than harvesting datasets that are may be able to represent these differences. Full article

(This article belongs to the Special Issue Application of Wearable Technology for Neurological Conditions)

► Show Figures

Figure 1

19 pages, 13306 KiB

Open AccessArticle

Detection of Sleep-Disordered Breathing in Patients with Spinal Cord Injury Using a Smartphone

by Yolanda Castillo-Escario, Hatice Kumru, Ignasi Ferrer-Lluis, Joan Vidal and Raimon Jané

Sensors 2021, 21(21), 7182; https://doi.org/10.3390/s21217182 - 29 Oct 2021

Cited by 1 | Viewed by 2431

Abstract

Patients with spinal cord injury (SCI) have an increased risk of sleep-disordered breathing (SDB), which can lead to serious comorbidities and impact patients’ recovery and quality of life. However, sleep tests are rarely performed on SCI patients, given their multiple health needs and the cost and complexity of diagnostic equipment. The objective of this study was to use a novel smartphone system as a simple non-invasive tool to monitor SDB in SCI patients. We recorded pulse oximetry, acoustic, and accelerometer data using a smartphone during overnight tests in 19 SCI patients and 19 able-bodied controls. Then, we analyzed these signals with automatic algorithms to detect desaturation, apnea, and hypopnea events and monitor sleep position. The apnea–hypopnea index (AHI) was significantly higher in SCI patients than controls (25 ± 15 vs. 9 ± 7, p < 0.001). We found that 63% of SCI patients had moderate-to-severe SDB (AHI ≥ 15) in contrast to 21% of control subjects. Most SCI patients slept predominantly in supine position, but an increased occurrence of events in supine position was only observed for eight patients. This study highlights the problem of SDB in SCI and provides simple cost-effective sleep monitoring tools to facilitate the detection, understanding, and management of SDB in SCI patients. Full article

(This article belongs to the Special Issue Application of Wearable Technology for Neurological Conditions)

► Show Figures

Figure 1

14 pages, 1532 KiB

Open AccessArticle

Wearable Inertial Gait Algorithms: Impact of Wear Location and Environment in Healthy and Parkinson’s Populations

by Yunus Celik, Sam Stuart, Wai Lok Woo and Alan Godfrey

Sensors 2021, 21(19), 6476; https://doi.org/10.3390/s21196476 - 28 Sep 2021

Cited by 10 | Viewed by 3317

Abstract

Wearable inertial measurement units (IMUs) are used in gait analysis due to their discrete wearable attachment and long data recording possibilities within indoor and outdoor environments. Previously, lower back and shin/shank-based IMU algorithms detecting initial and final contact events (ICs-FCs) were developed and validated on a limited number of healthy young adults (YA), reporting that both IMU wear locations are suitable to use during indoor and outdoor gait analysis. However, the impact of age (e.g., older adults, OA), pathology (e.g., Parkinson′s Disease, PD) and/or environment (e.g., indoor vs. outdoor) on algorithm accuracy have not been fully investigated. Here, we examined IMU gait data from 128 participants (72-YA, 20-OA, and 36-PD) to thoroughly investigate the suitability of ICs-FCs detection algorithms (1 × lower back and 1 × shin/shank-based) for quantifying temporal gait characteristics depending on IMU wear location and walking environment. The level of agreement between algorithms was investigated for different cohorts and walking environments. Although mean temporal characteristics from both algorithms were significantly correlated for all groups and environments, subtle but characteristically nuanced differences were observed between cohorts and environments. The lowest absolute agreement level was observed in PD (ICC_2,1 = 0.979, 0.806, 0.730, 0.980) whereas highest in YA (ICC_2,1 = 0.987, 0.936, 0.909, 0.989) for mean stride, stance, swing, and step times, respectively. Absolute agreement during treadmill walking (ICC_2,1 = 0.975, 0.914, 0.684, 0.945), indoor walking (ICC_2,1 = 0.987, 0.936, 0.909, 0.989) and outdoor walking (ICC_2,1 = 0.998, 0.940, 0.856, 0.998) was found for mean stride, stance, swing, and step times, respectively. Findings of this study suggest that agreements between algorithms are sensitive to the target cohort and environment. Therefore, researchers/clinicians should be cautious while interpreting temporal parameters that are extracted from inertial sensors-based algorithms especially for those with a neurological condition. Full article

(This article belongs to the Special Issue Application of Wearable Technology for Neurological Conditions)

► Show Figures

Figure 1

15 pages, 1221 KiB

Open AccessArticle

Daily Life Upper Limb Activity for Patients with Match and Mismatch between Observed Function and Perceived Activity in the Chronic Phase Post Stroke

by Bea Essers, Marjan Coremans, Janne Veerbeek, Andreas Luft and Geert Verheyden

Sensors 2021, 21(17), 5917; https://doi.org/10.3390/s21175917 - 2 Sep 2021

Cited by 8 | Viewed by 2302

Abstract

We investigated actual daily life upper limb (UL) activity in relation to observed UL motor function and perceived UL activity in chronic stroke in order to better understand and improve UL activity in daily life. In 60 patients, we collected (1) observed UL motor function (Fugl-Meyer Assessment (FMA-UE)), (2) perceived UL activity (hand subscale of the Stroke Impact Scale (SIS-Hand)), and (3) daily life UL activity (bilateral wrist-worn accelerometers for 72 h) data. Data were compared between two groups of interest, namely (1) good observed (FMA-UE >50) function and good perceived (SIS-Hand >75) activity (good match, n = 16) and (2) good observed function but low perceived (SIS-Hand ≤75) activity (mismatch, n = 15) with Mann–Whitney U analysis. The mismatch group only differed from the good match group in perceived UL activity (median (Q1–Q3) = 50 (30–70) versus 93 (85–100); p < 0.001). Despite similar observed UL motor function and other clinical characteristics, the affected UL in the mismatch group was less active in daily life compared to the good match group (p = 0.013), and the contribution of the affected UL compared to the unaffected UL for each second of activity (magnitude ratio) was lower (p = 0.022). We conclude that people with chronic stroke with low perceived UL activity indeed tend to use their affected UL less in daily life despite good observed UL motor function. Full article

(This article belongs to the Special Issue Application of Wearable Technology for Neurological Conditions)

► Show Figures

Figure 1

17 pages, 1978 KiB

Open AccessArticle

An Electro-Oculogram Based Vision System for Grasp Assistive Devices—A Proof of Concept Study

by Rinku Roy, Manjunatha Mahadevappa and Kianoush Nazarpour

Sensors 2021, 21(13), 4515; https://doi.org/10.3390/s21134515 - 1 Jul 2021

Viewed by 2581

Abstract

Humans typically fixate on objects before moving their arm to grasp the object. Patients with ALS disorder can also select the object with their intact eye movement, but are unable to move their limb due to the loss of voluntary muscle control. Though several research works have already achieved success in generating the correct grasp type from their brain measurement, we are still searching for fine controll over an object with a grasp assistive device (orthosis/exoskeleton/robotic arm). Object orientation and object width are two important parameters for controlling the wrist angle and the grasp aperture of the assistive device to replicate a human-like stable grasp. Vision systems are already evolved to measure the geometrical attributes of the object to control the grasp with a prosthetic hand. However, most of the existing vision systems are integrated with electromyography and require some amount of voluntary muscle movement to control the vision system. Due to that reason, those systems are not beneficial for the users with brain-controlled assistive devices. Here, we implemented a vision system which can be controlled through the human gaze. We measured the vertical and horizontal electrooculogram signals and controlled the pan and tilt of a cap-mounted webcam to keep the object of interest in focus and at the centre of the picture. A simple ‘signature’ extraction procedure was also utilized to reduce the algorithmic complexity and system storage capacity. The developed device has been tested with ten healthy participants. We approximated the object orientation and the size of the object and determined an appropriate wrist orientation angle and the grasp aperture size within 22 ms. The combined accuracy exceeded 75%. The integration of the proposed system with the brain-controlled grasp assistive device and increasing the number of grasps can offer more natural manoeuvring in grasp for ALS patients. Full article

(This article belongs to the Special Issue Application of Wearable Technology for Neurological Conditions)

► Show Figures

Figure 1

Other

Jump to: Research

20 pages, 519 KiB

Open AccessSystematic Review

Wearable and Portable GPS Solutions for Monitoring Mobility in Dementia: A Systematic Review

by Anisha Cullen, Md Khadimul Anam Mazhar, Matthew D. Smith, Fiona E. Lithander, Mícheál Ó Breasail and Emily J. Henderson

Sensors 2022, 22(9), 3336; https://doi.org/10.3390/s22093336 - 27 Apr 2022

Cited by 13 | Viewed by 4449

Abstract

Dementia is the most common neurodegenerative disorder globally. Disease progression is marked by declining cognitive function accompanied by changes in mobility. Increased sedentary behaviour and, conversely, wandering and becoming lost are common. Global positioning system (GPS) solutions are increasingly used by caregivers to locate missing people with dementia (PwD) but also offer a non-invasive means of monitoring mobility patterns in PwD. We performed a systematic search across five databases to identify papers published since 2000, where wearable or portable GPS was used to monitor mobility in patients with common dementias or mild cognitive impairment (MCI). Disease and GPS-specific vocabulary were searched singly, and then in combination, identifying 3004 papers. Following deduplication, we screened 1972 papers and retained 17 studies after a full-text review. Only 1/17 studies used a wrist-worn GPS solution, while all others were variously located on the patient. We characterised the studies using a conceptual framework, finding marked heterogeneity in the number and complexity of reported GPS-derived mobility outcomes. Duration was the most frequently reported category of mobility reported (15/17), followed by out of home (14/17), and stop and trajectory (both 10/17). Future research would benefit from greater standardisation and harmonisation of reporting which would enable GPS-derived measures of mobility to be incorporated more robustly into clinical trials. Full article

(This article belongs to the Special Issue Application of Wearable Technology for Neurological Conditions)

► Show Figures

Figure 1

16 pages, 564 KiB

Open AccessSystematic Review

Use of Robotic Devices for Gait Training in Patients Diagnosed with Multiple Sclerosis: Current State of the Art

by Sagrario Pérez-de la Cruz

Sensors 2022, 22(7), 2580; https://doi.org/10.3390/s22072580 - 28 Mar 2022

Cited by 5 | Viewed by 2443

Abstract

Multiple sclerosis (MS) is a neurodegenerative disease that produces alterations in balance and gait in most patients. Robot-assisted gait training devices have been proposed as a complementary approach to conventional rehabilitation treatment as a means of improving these alterations. The aim of this study was to investigate the available scientific evidence on the benefits of the use of robotics in the physiotherapy treatment in people with MS. A systematic review of randomized controlled trials was performed. Studies from the last five years on walking in adults with MS were included. The PEDro scale was used to assess the methodological quality of the included studies, and the Jadad scale was used to assess the level of evidence and the degree of recommendation. Seventeen studies met the eligibility criteria. For the improvement of gait speed, robotic devices do not appear to be superior, compared to the rest of the interventions evaluated. The methodological quality of the studies was moderate–low. For this reason, robot-assisted gait training is considered just as effective as conventional rehabilitation training for improving gait in people with MS. Full article

(This article belongs to the Special Issue Application of Wearable Technology for Neurological Conditions)

► Show Figures

Figure 1

13 pages, 6680 KiB

Open AccessPerspective

Metadata Framework to Support Deployment of Digital Health Technologies in Clinical Trials in Parkinson’s Disease

by Derek L. Hill, Diane Stephenson, Jordan Brayanov, Kasper Claes, Reham Badawy, Sakshi Sardar, Katherine Fisher, Susan J. Lee, Anthony Bannon, George Roussos, Tairmae Kangarloo, Viktorija Terebaite, Martijn L. T. M. Müller, Roopal Bhatnagar, Jamie L. Adams, E. Ray Dorsey and Josh Cosman

Sensors 2022, 22(6), 2136; https://doi.org/10.3390/s22062136 - 9 Mar 2022

Cited by 4 | Viewed by 3748

Abstract

Sensor data from digital health technologies (DHTs) used in clinical trials provides a valuable source of information, because of the possibility to combine datasets from different studies, to combine it with other data types, and to reuse it multiple times for various purposes. To date, there exist no standards for capturing or storing DHT biosensor data applicable across modalities and disease areas, and which can also capture the clinical trial and environment-specific aspects, so-called metadata. In this perspectives paper, we propose a metadata framework that divides the DHT metadata into metadata that is independent of the therapeutic area or clinical trial design (concept of interest and context of use), and metadata that is dependent on these factors. We demonstrate how this framework can be applied to data collected with different types of DHTs deployed in the WATCH-PD clinical study of Parkinson’s disease. This framework provides a means to pre-specify and therefore standardize aspects of the use of DHTs, promoting comparability of DHTs across future studies. Full article

(This article belongs to the Special Issue Application of Wearable Technology for Neurological Conditions)

► Show Figures

Figure 1

18 pages, 614 KiB

Open AccessSystematic Review

Wearable GPS and Accelerometer Technologies for Monitoring Mobility and Physical Activity in Neurodegenerative Disorders: A Systematic Review

by Mícheál Ó Breasail, Bijetri Biswas, Matthew D. Smith, Md Khadimul A. Mazhar, Emma Tenison, Anisha Cullen, Fiona E. Lithander, Anne Roudaut and Emily J. Henderson

Sensors 2021, 21(24), 8261; https://doi.org/10.3390/s21248261 - 10 Dec 2021

Cited by 19 | Viewed by 6147

Abstract

Neurodegenerative disorders (NDDs) constitute an increasing global burden and can significantly impair an individual’s mobility, physical activity (PA), and independence. Remote monitoring has been difficult without relying on diaries/questionnaires which are more challenging for people with dementia to complete. Wearable global positioning system (GPS) sensors and accelerometers present a cost-effective and noninvasive way to passively monitor mobility and PA. In addition, changes in sensor-derived outcomes (such as walking behaviour, sedentary, and active activity) may serve as potential biomarkers of disease onset, progression, and response to treatment. We performed a systematic search across four databases to identify papers published within the past 5 years, in which wearable GPS or accelerometers were used to monitor mobility or PA in patients with common NDDs (Parkinson’s disease, Alzheimer’s disease, motor neuron diseases/amyotrophic lateral sclerosis, vascular parkinsonism, and vascular dementia). Disease and technology-specific vocabulary were searched singly, and then in combination, identifying 4985 papers. Following deduplication, we screened 3115 papers and retained 28 studies following a full text review. One study used wearable GPS and accelerometers, while 27 studies used solely accelerometers in NDDs. GPS-derived measures had been validated against current gold standard measures in one Parkinson’s cohort, suggesting that the technology may be applicable to other NDDs. In contrast, accelerometers are widely utilised in NDDs and have been operationalised in well-designed clinical trials. Full article

(This article belongs to the Special Issue Application of Wearable Technology for Neurological Conditions)

► Show Figures