scholarly journals Predicting Fall Counts Using Wearable Sensors: A Novel Digital Biomarker for Parkinson’s Disease

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 54
Author(s):  
Barry R. Greene ◽  
Isabella Premoli ◽  
Killian McManus ◽  
Denise McGrath ◽  
Brian Caulfield
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Inertial Sensor ◽  
Wearable Sensors ◽  
Strong Association ◽  
Sensor Data ◽  
Clinical Population ◽  
Timed Up And Go ◽  
Falls Risk ◽  
Serious Injury

People with Parkinson’s disease (PD) experience significant impairments to gait and balance; as a result, the rate of falls in people with Parkinson’s disease is much greater than that of the general population. Falls can have a catastrophic impact on quality of life, often resulting in serious injury and even death. The number (or rate) of falls is often used as a primary outcome in clinical trials on PD. However, falls data can be unreliable, expensive and time-consuming to collect. We sought to validate and test a novel digital biomarker for PD that uses wearable sensor data obtained during the Timed Up and Go (TUG) test to predict the number of falls that will be experienced by a person with PD. Three datasets, containing a total of 1057 (671 female) participants, including 71 previously diagnosed with PD, were included in the analysis. Two statistical approaches were considered in predicting falls counts: the first based on a previously reported falls risk assessment algorithm, and the second based on elastic net and ensemble regression models. A predictive model for falls counts in PD showed a mean R2 value of 0.43, mean error of 0.42 and a mean correlation of 30% when the results were averaged across two independent sets of PD data. The results also suggest a strong association between falls counts and a previously reported inertial sensor-based falls risk estimate. In addition, significant associations were observed between falls counts and a number of individual gait and mobility parameters. Our preliminary research suggests that the falls counts predicted from the inertial sensor data obtained during a simple walking task have the potential to be developed as a novel digital biomarker for PD, and this deserves further validation in the targeted clinical population.

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

2021 ◽  
Vol 8 (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.

Combined Vision and Wearable Sensors-based System for Movement Analysis in Rehabilitation

2017 ◽  
Vol 56 (02) ◽  
pp. 95-111 ◽  
Author(s):  
Tihomir V. Ilić ◽  
Slađan Milanović ◽  
Veljko Potkonjak ◽  
Aleksandar Rodić ◽  
José Santos-Victor ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Performance Indicators ◽  
Wearable Sensors ◽  
Movement Analysis ◽  
Assessment Tools ◽  
Sensor Data ◽  
Disease Stage ◽  
Movement Performance ◽  
Rehabilitation Exercises

SummaryBackground: Traditional rehabilitation sessions are often a slow, tedious, disempowering and non-motivational process, supported by clinical assessment tools, i.e. evaluation scales that are prone to subjective rating and imprecise interpretation of patient’s performance. Poor patient motivation and insufficient accuracy are thus critical factors that can be improved by new sensing/processing technologies.Objectives: We aim to develop a portable and affordable system, suitable for home rehabilitation, which combines vision-based and wearable sensors. We introduce a novel approach for examining and characterizing the rehabilitation movements, using quantitative descriptors. We propose new Movement Performance Indicators (MPIs) that are extracted directly from sensor data and quantify the symmetry, velocity, and acceleration of the movement of different body/hand parts, and that can potentially be used by therapists for diagnosis and progress assessment.Methods: First, a set of rehabilitation exercises is defined, with the supervision of neurologists and therapists for the specific case of Parkinson’s disease. It comprises full-body movements measured with a Kinect device and fine hand movements, acquired with a data glove. Then, the sensor data is used to compute 25 Movement Performance Indicators, to assist the diagnosis and progress monitoring (assessing the disease stage) in Parkinson’s disease. A kinematic hand model is developed for data verification and as an additional resource for extracting supplementary movement information.Results: Our results show that the proposed Movement Performance Indicators are relevant for the Parkinson’s disease assessment. This is further confirmed by correlation of the proposed indicators with clinical tapping test and UPDRS clinical scale. Classification results showed the potential of these indicators to discriminate between the patients and controls, as well as between the stages that characterize the evolution of the disease.Conclusions: The proposed sensor system, along with the developed approach for rehabilitation movement analysis have a significant potential to support and advance traditional rehabilitation therapy. The main impact of our work is two-fold: (i) the proposition of an approach for supporting the therapists during the diagnosis and monitoring evaluations by reducing subjectivity and imprecision, and (ii) offering the possibility of the system to be used at home for rehabilitation exercises in between sessions with doctors and therapists.

scholarly journals The Smart-Insole Dataset: Gait Analysis Using Wearable Sensors with a Focus on Elderly and Parkinson’s Patients

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2821
Author(s):  
Chariklia Chatzaki ◽  
Vasileios Skaramagkas ◽  
Nikolaos Tachos ◽  
Georgios Christodoulakis ◽  
Evangelia Maniadi ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Gait Analysis ◽  
Rating Scale ◽  
Wearable Sensors ◽  
The Elderly ◽  
Detection Algorithm ◽  
Timed Up And Go ◽  
Computer Scientists ◽  
Smart Insole

Gait analysis is crucial for the detection and management of various neurological and musculoskeletal disorders. The identification of gait events is valuable for enhancing gait analysis, developing accurate monitoring systems, and evaluating treatments for pathological gait. The aim of this work is to introduce the Smart-Insole Dataset to be used for the development and evaluation of computational methods focusing on gait analysis. Towards this objective, temporal and spatial characteristics of gait have been estimated as the first insight of pathology. The Smart-Insole dataset includes data derived from pressure sensor insoles, while 29 participants (healthy adults, elderly, Parkinson’s disease patients) performed two different sets of tests: The Walk Straight and Turn test, and a modified version of the Timed Up and Go test. A neurologist specialized in movement disorders evaluated the performance of the participants by rating four items of the MDS-Unified Parkinson’s Disease Rating Scale. The annotation of the dataset was performed by a team of experienced computer scientists, manually and using a gait event detection algorithm. The results evidence the discrimination between the different groups, and the verification of established assumptions regarding gait characteristics of the elderly and patients suffering from Parkinson’s disease.

scholarly journals Validation of quantitative gait analysis systems for Parkinson’s disease for use in supervised and unsupervised environments

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sara Alberto ◽  
Sílvia Cabral ◽  
João Proença ◽  
Filipa Pona-Ferreira ◽  
Mariana Leitão ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Gait Analysis ◽  
Inertial Sensor ◽  
Low Cost ◽  
Wearable Sensors ◽  
Smartphone Application ◽  
Control Group ◽  
Wearable Systems ◽  
Full Body

Abstract Background Gait impairments are among the most common and impactful symptoms of Parkinson’s disease (PD). Recent technological advances aim to quantify these impairments using low-cost wearable systems for use in either supervised clinical consultations or long-term unsupervised monitoring of gait in ecological environments. However, very few of these wearable systems have been validated comparatively to a criterion of established validity. Objective We developed two movement analysis solutions (3D full-body kinematics based on inertial sensors, and a smartphone application) in which validity was assessed versus the optoelectronic criterion in a population of PD patients. Methods Nineteen subjects with PD (7 female) participated in the study (age: 62 ± 12.27 years; disease duration: 6.39 ± 3.70 years; HY: 2 ± 0.23). Each participant underwent a gait analysis whilst barefoot, at a self-selected speed, for a distance of 3 times 10 m in a straight line, assessed simultaneously with all three systems. Results Our results show excellent agreement between either solution and the optoelectronic criterion. Both systems differentiate between PD patients and healthy controls, and between PD patients in ON or OFF medication states (normal difference distributions pooled from published research in PD patients in ON and OFF states that included an age-matched healthy control group). Fair to high waveform similarity and mean absolute errors below the mean relative orientation accuracy of the equipment were found when comparing the angular kinematics between the full-body inertial sensor-based system and the optoelectronic criterion. Conclusions We conclude that the presented solutions produce accurate results and can capture clinically relevant parameters using commodity wearable sensors or a simple smartphone. This validation will hopefully enable the adoption of these systems for supervised and unsupervised gait analysis in clinical practice and clinical trials.

scholarly journals Quantitative and qualitative gait assessments in Parkinson’s disease patients

2014 ◽  
Vol 71 (9) ◽  
pp. 809-816 ◽  
Author(s):  
Milica Djuric-Jovicic ◽  
Nenad Jovicic ◽  
Sasa Radovanovic ◽  
Nikola Kresojevic ◽  
Vladimir Kostic ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Inertial Sensor ◽  
Freezing Of Gait ◽  
Assessment System ◽  
Sensor Data ◽  
Postural Adjustment ◽  
Gait Assessment ◽  
Graphical Tool ◽  
Gait Disturbances

Background/Aim. Postural impairments and gait disorders in Parkinson's disease (PD) affect limits of stability, impaire postural adjustment, and evoke poor responses to perturbation. In the later stage of the disease, some patients can suffer from episodic features such as freezing of gait (FOG). Objective gait assessment and monitoring progress of the disease can give clinicians and therapist important information about changes in gait pattern and potential gait deviations, in order to prevent concomitant falls. The aim of this study was to propose a method for identification of freezing episodes and gait disturbances in patients with PD. A wireless inertial sensor system can be used to provide follow-up of the treatment effects or progress of the disease. Methods. The system is simple for mounting a subject, comfortable, simple for installing and recording, reliable and provides high-quality sensor data. A total of 12 patients were recorded and tested. Software calculates various gait parameters that could be estimated. User friendly visual tool provides information about changes in gait characteristics, either in a form of spectrogram or by observing spatiotemporal parameters. Based on these parameters, the algorithm performs classification of strides and identification of FOG types. Results. The described stride classification was merged with an algorithm for stride reconstruction resulting in a useful graphical tool that allows clinicians to inspect and analyze subject?s movements. Conclusion. The described gait assessment system can be used for detection and categorization of gait disturbances by applying rule-based classification based on stride length, stride time, and frequency of the shank segment movements. The method provides an valuable graphical interface which is easy to interpret and provides clinicians and therapists with valuable information regarding the temporal changes in gait.

scholarly journals Monitoring Parkinson’s disease symptoms during daily life: a feasibility study

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Margot Heijmans ◽  
Jeroen G. V. Habets ◽  
Christian Herff ◽  
Jos Aarts ◽  
An Stevens ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Monitoring System ◽  
Daily Life ◽  
Wearable Sensors ◽  
Ground Truth ◽  
Sensor Data ◽  
Disease Monitoring ◽  
Subjective Character ◽  
Disease Symptoms

Abstract Parkinson’s disease symptoms are most often charted using the MDS-UPDRS. Limitations of this approach include the subjective character of the assessments and a discrepant performance in the clinic compared to the home situation. Continuous monitoring using wearable devices is believed to eventually replace this golden standard, but measurements often lack a parallel ground truth or are only tested in lab settings. To overcome these limitations, this study explores the feasibility of a newly developed Parkinson’s disease monitoring system, which aims to measure Parkinson’s disease symptoms during daily life by combining wearable sensors with an experience sampling method application. Twenty patients with idiopathic Parkinson’s disease participated in this study. During a period of two consecutive weeks, participants had to wear three wearable sensors and had to complete questionnaires at seven semi-random moments per day on their mobile phone. Wearable sensors collected objective movement data, and the questionnaires containing questions about amongst others Parkinson’s disease symptoms served as parallel ground truth. Results showed that participants wore the wearable sensors during 94% of the instructed timeframe and even beyond. Furthermore, questionnaire completion rates were high (79,1%) and participants evaluated the monitoring system positively. A preliminary analysis showed that sensor data could reliably predict subjectively reported OFF moments. These results show that our Parkinson’s disease monitoring system is a feasible method to use in a diverse Parkinson’s disease population for at least a period of two weeks. For longer use, the monitoring system may be too intense and wearing comfort needs to be optimized.

scholarly journals EFFECTS OF VIRTUAL REALITY ON PARKINSONIAN GAIT: BLIND CONTROLLED RANDOMIZED CLINICAL TRIAL PROTOCOL

2020 ◽  
pp. 1-7
Author(s):  
Gileno Edu Lameira de Melo ◽  
Renata Calhes Franco de Moura ◽  
Jamile Benite Palma Lopes ◽  
Paulo Roberto Fonseca Junior ◽  
Roberta Delasta Lazzari ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Clinical Trial ◽  
Parkinson's Disease ◽  
Virtual Reality ◽  
Rating Scale ◽  
Inertial Sensor ◽  
Gait Training ◽  
Treadmill Training ◽  
Timed Up And Go ◽  
Parametric Data

Background: Parkinson’s disease (PD) is a neurodegenerative condition that exerts a negative influence on postural control and mobility, predominantly among older adults, affecting mobility and increasing the risk of slipping, tripping and falls. Objectives: Determine the effectiveness of gait training with the aid of virtual reality on balance and gait in comparison to treadmill training and conventional gait training for subjects with PD. Methods/design: A prospective, randomized, controlled blind, clinical trial is proposed. The sample will comprise 45 individuals with Parkinson’s disease randomly allocated to three groups (Control: conventional gait training; Experimental I: treadmill training; and Experimental II: gait training involving virtual reality). Evaluations will be conducted on four occasions: 1) pre-intervention; 2) immediately after one session; 3) post-intervention; and 4) one month after last session. The evaluations will involve the Berg Balance Scale, Tinetti Test, Unified Parkinson’s Disease Rating Scale and Hoehn and Yahr Scale. The G-WALK (BTS) inertial sensor will be used for the evaluation of spatiotemporal gait variables, the Timed Up and Go test and the Six-Minute Walk Test. Training will involve 20-minute sessions held three times per week for four consecutive weeks. The results will be analyzed statistically by two-way ANOVA for the data with parametric distribution or Friedman’s two-way ANOVA for non-parametric data (α<0.05). Conclusion: The proposed study will compare gait conventional training, treadmill training and training with virtual reality with regard to improvements in gait and balance in patients with PD. The results will be published and will contribute evidence on the use of these interventions for individuals with PD.

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