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 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.

Experience of Parkinson’s Disease Symptoms During Daily Life Activities

2015 ◽  
Vol 69 (Suppl. 1) ◽  
pp. 6911505111p1 ◽  
Author(s):  
Linda Tickle-Degnen ◽  
Shih-yu Lur ◽  
Jessica Pickett
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Daily Life ◽  
Disease Symptoms ◽  
Daily Life Activities

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 Remote Parkinson's disease monitoring system: from smartphone to cloud platform

2019 ◽  
pp. 21-25
Author(s):  
О.В. Бережний ◽  
T.O. Білобородова ◽  
І.С. Скарга-Бандурова
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Monitoring System ◽  
Data Transmission ◽  
Cloud Storage ◽  
Smartphone Application ◽  
Sensor Data ◽  
Common Symptom ◽  
Cloud Platform ◽  
Smartphone Sensors

Parkinson's disease (PD) is a slowly progressive disorder that affects movement, muscle control, and balance. The earlier treatment can prevent the disease from developing and to prolongate the diseases prodromal phase. In this context, home monitoring services are potentially powerful tools for remote diagnosis and can improve healthcare services. Tremor is the most common symptom of a PD disorder and it has several advantages for continuous PD symptoms monitoring. The developing of solution based on smartphone sensors that allow remote monitoring of the monitored user is present. The connection between the smartphone application and cloud platform for smartphone sensors data transmission for early tremor symptoms detection is developed. It includesdeveloping of configuration of smartphone application for sensor data transmission and developing of configuration of a cloud platform for tremor symptoms monitoring. The active tests were developed to capture a motor disorder, that indicates PD symptom such as tremor.  Initial trials of the developing demonstrated that the monitoring system has the ability to real-time data acquisition and transmission using smartphone sensors and cloud storage. The connection settings developed for the system proved to be efficient when sensor data transmitted from the smartphone to cloud storage. The period of time required to transfer data to the cloud equal to the period of time less than one second.

Wearable sensors in the diagnosis and study of Parkinson’s disease symptoms: a systematic review

2021 ◽  
pp. 1-14
Author(s):  
Andrea C. Albán-Cadena ◽  
Fernando Villalba-Meneses ◽  
Kevin O. Pila-Varela ◽  
Alejandro Moreno-Calvo ◽  
Carlos P. Villalba-Meneses ◽  
...  
Keyword(s):  
Systematic Review ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Wearable Sensors ◽  
Disease Symptoms

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 Validation of IMU-based gait event detection during curved walking and turning in older adults and Parkinson’s Disease patients

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Robbin Romijnders ◽  
Elke Warmerdam ◽  
Clint Hansen ◽  
Julius Welzel ◽  
Gerhard Schmidt ◽  
...  
Keyword(s):  
Older Adults ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Daily Life ◽  
Neurological Diseases ◽  
Detection Performance ◽  
Measurement Unit ◽  
Straight Line ◽  
Optical Motion ◽  
Task Conditions

Abstract Background Identification of individual gait events is essential for clinical gait analysis, because it can be used for diagnostic purposes or tracking disease progression in neurological diseases such as Parkinson’s disease. Previous research has shown that gait events can be detected from a shank-mounted inertial measurement unit (IMU), however detection performance was often evaluated only from straight-line walking. For use in daily life, the detection performance needs to be evaluated in curved walking and turning as well as in single-task and dual-task conditions. Methods Participants (older adults, people with Parkinson’s disease, or people who had suffered from a stroke) performed three different walking trials: (1) straight-line walking, (2) slalom walking, (3) Stroop-and-walk trial. An optical motion capture system was used a reference system. Markers were attached to the heel and toe regions of the shoe, and participants wore IMUs on the lateral sides of both shanks. The angular velocity of the shank IMUs was used to detect instances of initial foot contact (IC) and final foot contact (FC), which were compared to reference values obtained from the marker trajectories. Results The detection method showed high recall, precision and F1 scores in different populations for both initial contacts and final contacts during straight-line walking (IC: recall $$=$$ = 100%, precision $$=$$ = 100%, F1 score $$=$$ = 100%; FC: recall $$=$$ = 100%, precision $$=$$ = 100%, F1 score $$=$$ = 100%), slalom walking (IC: recall $$=$$ = 100%, precision $$\ge$$ ≥ 99%, F1 score $$=$$ = 100%; FC: recall $$=$$ = 100%, precision $$\ge$$ ≥ 99%, F1 score $$=$$ = 100%), and turning (IC: recall $$\ge$$ ≥ 85%, precision $$\ge$$ ≥ 95%, F1 score $$\ge$$ ≥ 91%; FC: recall $$\ge$$ ≥ 84%, precision $$\ge$$ ≥ 95%, F1 score $$\ge$$ ≥ 89%). Conclusions Shank-mounted IMUs can be used to detect gait events during straight-line walking, slalom walking and turning. However, more false events were observed during turning and more events were missed during turning. For use in daily life we recommend identifying turning before extracting temporal gait parameters from identified gait events.

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