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 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 Early Detection of Freezing of Gait during Walking Using Inertial Measurement Unit and Plantar Pressure Distribution Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2246
Author(s):  
Scott Pardoel ◽  
Gaurav Shalin ◽  
Julie Nantel ◽  
Edward D. Lemaire ◽  
Jonathan Kofman
Keyword(s):  
Early Detection ◽  
Plantar Pressure ◽  
Inertial Measurement Unit ◽  
Binary Classification ◽  
Freezing Of Gait ◽  
Pressure Sensors ◽  
Measurement Unit ◽  
Distribution Data ◽  
Inertial Measurement ◽  
Plantar Pressure Distribution

Freezing of gait (FOG) is a sudden and highly disruptive gait dysfunction that appears in mid to late-stage Parkinson’s disease (PD) and can lead to falling and injury. A system that predicts freezing before it occurs or detects freezing immediately after onset would generate an opportunity for FOG prevention or mitigation and thus enhance safe mobility and quality of life. This research used accelerometer, gyroscope, and plantar pressure sensors to extract 861 features from walking data collected from 11 people with FOG. Minimum-redundancy maximum-relevance and Relief-F feature selection were performed prior to training boosted ensembles of decision trees. The binary classification models identified Total-FOG or No FOG states, wherein the Total-FOG class included data windows from 2 s before the FOG onset until the end of the FOG episode. Three feature sets were compared: plantar pressure, inertial measurement unit (IMU), and both plantar pressure and IMU features. The plantar-pressure-only model had the greatest sensitivity and the IMU-only model had the greatest specificity. The best overall model used the combination of plantar pressure and IMU features, achieving 76.4% sensitivity and 86.2% specificity. Next, the Total-FOG class components were evaluated individually (i.e., Pre-FOG windows, Freeze windows, transition windows between Pre-FOG and Freeze). The best model detected windows that contained both Pre-FOG and FOG data with 85.2% sensitivity, which is equivalent to detecting FOG less than 1 s after the freeze began. Windows of FOG data were detected with 93.4% sensitivity. The IMU and plantar pressure feature-based model slightly outperformed models that used data from a single sensor type. The model achieved early detection by identifying the transition from Pre-FOG to FOG while maintaining excellent FOG detection performance (93.4% sensitivity). Therefore, if used as part of an intelligent, real-time FOG identification and cueing system, even if the Pre-FOG state were missed, the model would perform well as a freeze detection and cueing system that could improve the mobility and independence of people with PD during their daily activities.

scholarly journals Kin-FOG: Automatic Simulated Freezing of Gait (FOG) Assessment System for Parkinson’s Disease

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2416 ◽  
Author(s):  
Sara Soltaninejad ◽  
Irene Cheng ◽  
Anup Basu
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Freezing Of Gait ◽  
The Elderly ◽  
Assessment System ◽  
Microsoft Kinect ◽  
Data Sets ◽  
Fog Prediction ◽  
Foot Joint ◽  
Rehabilitation Clinic

Parkinson’s disease (PD) is one of the leading neurological disorders in the world with an increasing incidence rate for the elderly. Freezing of Gait (FOG) is one of the most incapacitating symptoms for PD especially in the later stages of the disease. FOG is a short absence or reduction of ability to walk for PD patients which can cause fall, reduction in patients’ quality of life, and even death. Existing FOG assessments by doctors are based on a patient’s diaries and experts’ manual video analysis which give subjective, inaccurate, and unreliable results. In the present research, an automatic FOG assessment system is designed for PD patients to provide objective information to neurologists about the FOG condition and the symptom’s characteristics. The proposed FOG assessment system uses an RGB-D sensor based on Microsoft Kinect V2 for capturing data for 5 healthy subjects who are trained to imitate the FOG phenomenon. The proposed FOG assessment system is called “Kin-FOG”. The analysis of foot joint trajectory of the motion captured by Kinect is used to find the FOG episodes. The evaluation of Kin-FOG is performed by two types of experiments, including: (1) simple walking (SW); and (2) walking with turning (WWT). Since the standing mode has features similar to a FOG episode, our Kin-FOG system proposes a method to distinguish between the FOG and standing episodes. Therefore, two general groups of experiments are conducted with standing state (WST) and without standing state (WOST). The gradient displacement of the angle between the foot and the ground is used as the feature for discriminating between FOG and standing modes. These experiments are conducted with different numbers of FOGs for getting reliable and general results. The Kin-FOG system reports the number of FOGs, their lengths, and the time slots when they occur. Experimental results demonstrate Kin-FOG has around 90% accuracy rate for FOG prediction in both experiments for different tasks (SW, WWT). The proposed Kin-FOG system can be used as a remote application at a patient’s home or a rehabilitation clinic for sending a neurologist the required FOG information. The reliability and generality of the proposed system will be evaluated for bigger data sets of actual PD subjects.

scholarly journals DeepFoG: An IMU-Based Detection of Freezing of Gait Episodes in Parkinson’s Disease Patients via Deep Learning

2021 ◽  
Vol 8 ◽  
Author(s):  
Thomas Bikias ◽  
Dimitrios Iakovakis ◽  
Stelios Hadjidimitriou ◽  
Vasileios Charisis ◽  
Leontios J. Hadjileontiadis
Keyword(s):  
Quality Of Life ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Life Stress ◽  
Freezing Of Gait ◽  
Measurement Unit ◽  
Effective Interventions ◽  
Risk Of Falls

Freezing of Gait (FoG) is a movement disorder that mostly appears in the late stages of Parkinson’s Disease (PD). It causes incapability of walking, despite the PD patient’s intention, resulting in loss of coordination that increases the risk of falls and injuries and severely affects the PD patient’s quality of life. Stress, emotional stimulus, and multitasking have been encountered to be associated with the appearance of FoG episodes, while the patient’s functionality and self-confidence are constantly deteriorating. This study suggests a non-invasive method for detecting FoG episodes, by analyzing inertial measurement unit (IMU) data. Specifically, accelerometer and gyroscope data from 11 PD subjects, as captured from a single wrist-worn IMU sensor during continuous walking, are processed via Deep Learning for window-based detection of the FoG events. The proposed approach, namely DeepFoG, was evaluated in a Leave-One-Subject-Out (LOSO) cross-validation (CV) and 10-fold CV fashion schemes against its ability to correctly estimate the existence or not of a FoG episode at each data window. Experimental results have shown that DeepFoG performs satisfactorily, as it achieves 83%/88% and 86%/90% sensitivity/specificity, for LOSO CV and 10-fold CV schemes, respectively. The promising performance of the proposed DeepFoG reveals the potentiality of single-arm IMU-based real-time FoG detection that could guide effective interventions via stimuli, such as rhythmic auditory stimulation (RAS) and hand vibration. In this way, DeepFoG may scaffold the elimination of risk of falls in PD patients, sustaining their quality of life in everyday living activities.

scholarly journals SELF STABILISING UTENSIL FOR PATIENTS WITH IMPAIRED MOTOR SKILLS

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Nikita Jain ◽  
Rishika I.S ◽  
Kusuma K.B ◽  
Dr. Manisha Shivaram Joshi
Keyword(s):  
Parkinson’S Disease ◽  
Motor Skills ◽  
Inertial Measurement Unit ◽  
Daily Activities ◽  
Stepper Motor ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Non Invasive ◽  
Invasive Device ◽  
The Given

- Nowadays, there is a substantial increase in the number of people getting affected by Parkinson's Disease. The degradations of motor skills severely affect the daily activities of patients such as eating, writing, dressing, etc. This project basically proposes a non-invasive device that helps the patient to eat and perform day to day chores independently using the given attachments. This device consists of Arduino which directs the two stepper motor to cancel the tremors detected by the Inertial Measurement Unit (MPU6050) containing 3 axis gyroscope and 3 axis accelerometer which senses these tremors.

scholarly journals Assessing inertial measurement unit locations for freezing of gait detection and patient preference

2021 ◽  
Author(s):  
Johanna O’Day ◽  
Marissa Lee ◽  
Kirsten Seagers ◽  
Shannon Hoffman ◽  
Ava Jih-Schiff ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Patient Preference ◽  
Freezing Of Gait ◽  
Home Monitoring ◽  
Inertial Measurement ◽  
Technical Performance ◽  
Percent Time ◽  
Time Freezing ◽  
At Home

ABSTRACTBackgroundFreezing of gait, a common symptom of Parkinson’s disease, presents as sporadic episodes in which an individual’s feet suddenly feel stuck to the ground. Inertial measurement units (IMUs) promise to enable at-home monitoring and personalization of therapy, but there is a lack of consensus on the number and location of IMUs for detecting freezing of gait. The purpose of this study was to assess IMU sets in the context of both freezing of gait detection performance and patient preference.MethodsSixteen people with Parkinson’s disease were surveyed about sensor preferences. Raw IMU data from seven people with Parkinson’s disease, wearing up to eleven sensors, were used to train convolutional neural networks to detect freezing of gait. Models trained with data from different sensor sets were assessed for technical performance; a best technical set and minimal IMU set were identified. Clinical utility was assessed by comparing model- and human-rater-determined percent time freezing and number of freezing events.ResultsThe best technical set consisted of three IMUs (lumbar and both ankles, AUROC = 0.83), all of which were rated highly wearable. The minimal IMU set consisted of a single ankle IMU (AUROC = 0.80). Correlations between these models and human raters were good to excellent for percent time freezing (ICC = 0.93, 0.89) and number of freezing events (ICC = 0.95, 0.86), for the best technical set and minimal IMU set, respectively.ConclusionsSeveral IMU sets consisting of three IMUs or fewer were highly rated for both technical performance and wearability, and more IMUs did not necessarily perform better in FOG detection. We openly share our data and software to further the adoption of a general, open-source model that uses raw signals and a standard sensor set for at-home monitoring of freezing of gait.

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