scholarly journals Ability of a Set of Trunk Inertial Indexes of Gait to Identify Gait Instability and Recurrent Fallers in Parkinson’s Disease

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3449
Author(s):  
Stefano Filippo Castiglia ◽  
Antonella Tatarelli ◽  
Dante Trabassi ◽  
Roberto De Icco ◽  
Valentina Grillo ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Healthy Subjects ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Largest Lyapunov Exponent ◽  
A Value ◽  
Trunk Acceleration ◽  
Posterior Direction ◽  
Stride Duration

The aims of this study were to assess the ability of 16 gait indices to identify gait instability and recurrent fallers in persons with Parkinson’s disease (pwPD), regardless of age and gait speed, and to investigate their correlation with clinical and kinematic variables. The trunk acceleration patterns were acquired during the gait of 55 pwPD and 55 age-and-speed matched healthy subjects using an inertial measurement unit. We calculated the harmonic ratios (HR), percent recurrence, and percent determinism (RQAdet), coefficient of variation, normalized jerk score, and the largest Lyapunov exponent for each participant. A value of ≤1.50 for the HR in the antero-posterior direction discriminated between pwPD at Hoehn and Yahr (HY) stage 3 and healthy subjects with a 67% probability, between pwPD at HY 3 and pwPD at lower HY stages with a 73% probability, and it characterized recurrent fallers with a 77% probability. Additionally, HR in the antero-posterior direction was correlated with pelvic obliquity and rotation. RQAdet in the antero-posterior direction discriminated between pwPD and healthy subjects with 67% probability, regardless of the HY stage, and was correlated with stride duration and cadence. Therefore, HR and RQAdet in the antero-posterior direction can both be used as age- and-speed-independent markers of gait instability.

THREE DAYS MONITORING OF ACTIVITIES OF DAILY LIVING AMONG YOUNG HEALTHY ADULTS AND PARKINSON’S DISEASE PATIENTS

2021 ◽  
Vol 57 (2) ◽  
pp. 177-183
Author(s):  
Seong Hyun Moon ◽  
◽  
Rahul Soangra ◽  
Christopher F. Frames ◽  
Thurmon E. Lockhart ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Activities Of Daily Living ◽  
Daily Living ◽  
Healthy Subjects ◽  
Neurodegenerative Disorder ◽  
Activity Level ◽  
Measurement Unit ◽  
Great Promise ◽  
Butterworth Filters

Parkinson’s Disease (PD) is a neurodegenerative disorder affecting the substantia nigra, which leads to more than half of PD patients are considered to be at high risk of falling. Recently, Inertial Measurement Unit (IMU) sensors have shown great promise in the classification of activities of daily living (ADL) such as walking, standing, sitting, and laying down, considered to be normal movement in daily life. Measuring physical activity level from longitudinal ADL monitoring among PD patients could provide insights into their fall mechanisms. In this study, six PD patients (mean age=74.3±6.5 years) and six young healthy subjects (mean age=19.7±2.7 years) were recruited. All the subjects were asked to wear the single accelerometer, DynaPort MM+ (Motion Monitor+, McRoberts BV, The Hague, Netherlands), with a sampling frequency of 100 Hz located at the L5-S1 spinal area for 3 days. Subjects maintained a log of activities they performed and only removed the sensor while showering or performing other aquatic activities. The resultant acceleration was filtered using high and low pass Butterworth filters to determine dynamic and stationary activities. As a result, it was found that healthy young subjects performed significantly more dynamic activities (13.2%) when compared to PD subjects (7%), in contrast, PD subjects (92.9%) had significantly more stationary activities than young healthy subjects (86.8%).

scholarly journals Deep learning for freezing of gait detection in Parkinson’s disease patients in their homes using a waist-worn inertial measurement unit

2018 ◽  
Vol 139 ◽  
pp. 119-131 ◽  
Author(s):  
Julià Camps ◽  
Albert Samà ◽  
Mario Martín ◽  
Daniel Rodríguez-Martín ◽  
Carlos Pérez-López ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Inertial Measurement Unit ◽  
Freezing Of Gait ◽  
Measurement Unit ◽  
Inertial Measurement

scholarly journals A Waist-Worn Inertial Measurement Unit for Long-Term Monitoring of Parkinson’s Disease Patients

Sensors ◽  
2017 ◽  
Vol 17 (4) ◽  
pp. 827 ◽  
Author(s):  
Daniel Rodríguez-Martín ◽  
Carlos Pérez-López ◽  
Albert Samà ◽  
Andreu Català ◽  
Joan Moreno Arostegui ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Long Term Monitoring ◽  
Term Monitoring

scholarly journals Wearable device intended for detection of fog episodes in Parkinson´s disease

2019 ◽  
Vol 13 (1) ◽  
pp. 10-16
Author(s):  
Laura Carolina Rozo Hoyos ◽  
Juan Pablo Pulgarín González ◽  
Paula Andrea Morales Fandiño ◽  
Jonathan Gallego Londoño
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Inertial Measurement Unit ◽  
Freezing Of Gait ◽  
Wearable Device ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Normal Gait ◽  
Fog Prediction ◽  
Advanced Stages

The episodes of Freezing of Gait (FOG) are a recurring symptom in people suffering from advanced stages of Parkinson's disease (PD). These are severe occurrences because they may cause falls to the patients, generating further traumas and concussions. In order to solve this yet ineffectively treated issue, this article describes the research that developed a device capable of predicting freezing episodes. On this project a wearable device was developed, which was able to predict freezing episodes based on the calculation of a freezing index (FI) determined by the signals obtained from an inertial measurement unit (IMU). This device was tested in three Patients and signals corresponding to normal gait and simulated Parkinson gait were taken. The results showed that FI obtained from Parkinson gait were much higher than those from a normal gait, validating this parameter as a key aspect in FOG prediction.      

scholarly journals Comparison of Physical Activity Level From Osteoporosis, Parkinson and Healthy Subjects

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 940-940
Author(s):  
Seong Hyun Moon ◽  
Thurmon Lockhart ◽  
Krupa Doshi
Keyword(s):  
Physical Activity ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Physical Activity Level ◽  
Healthy Subjects ◽  
Activity Level ◽  
Measurement Unit ◽  
Activity Levels ◽  
Significant Difference ◽  
Physical Activity Levels

Abstract Lifestyle at the habitation immensely affects the progression of various illnesses, such as Osteoporosis and Parkinson’s disease (PD). These disorders lead patients to a sedentary lifestyle and result in significantly less movement compared to the average healthy individual. The combination of these backgrounds escalates the percentage of fall incidents. Quantifying physical activity levels from longitudinal Activities of Daily Living (ADL) data of these disease patients could stipulate intuition of their fall mechanisms. The objective of this study is to compare the osteoporosis, Parkinson's disease, and healthy group’s physical activity level from their ADL. For this study total of eighteen subjects participated (healthy=6, osteoporosis=6, PD=6). The result indicated that the dynamic physical activity level for the healthy subject was 13.2%, the osteoporosis subject was 7.9%, and the PD subject was 7.0%. This indicates that there was a significant decline in physical activity level for the PD compared to healthy subjects (P=0.0024*). Also, a comparison between healthy and osteoporosis subjects showed a significant difference (P=0.0066*). Lastly, the physical activity level of PD and osteoporosis subjects did not have a significant difference among them (P=0.6276). The aim of this study was to evaluate the physical activity level of the osteoporosis, PD, and healthy subjects. The systematic approach of collecting physical activity levels with the Inertial Measurement Unit (IMU) device allowed researchers to collect the quantitative data of ADL. In this experiment, healthy subjects were significantly more physically active compared to osteoporosis and PD patients.

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.

Sign in / Sign up

Export Citation Format

Share Document