scholarly journals Feasibility of a Sensor-Based Gait Event Detection Algorithm for Triggering Functional Electrical Stimulation during Robot-Assisted Gait Training

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4804 ◽  
Author(s):  
Andreas Schicketmueller ◽  
Georg Rose ◽  
Marc Hofmann
Keyword(s):  
Electrical Stimulation ◽  
Functional Electrical Stimulation ◽  
Event Detection ◽  
Gait Training ◽  
Detection Algorithm ◽  
Inertial Measurement Units ◽  
Robot Assisted ◽  
Rehabilitation Process ◽  
Inertial Measurement ◽  
Measurement Units

Technologies such as robot-assisted gait trainers or functional electrical stimulation can improve the rehabilitation process of people affected with gait disorders due to stroke or other neurological defects. By combining both technologies, the potential disadvantages of each technology could be compensated and simultaneously, therapy effects could be improved. Thus, an algorithm was designed that aims to detect the gait cycle of a robot-assisted gait trainer. Based on movement data recorded with inertial measurement units, gait events can be detected. These events can further be used to trigger functional electrical stimulation. This novel setup offers the possibility of equipping a broad range of potential robot-assisted gait trainers with functional electrical stimulation. The aim of this paper in particular was to test the feasibility of a system using inertial measurement units for gait event detection during robot-assisted gait training. Thus, a 39-year-old healthy male adult executed a total of six training sessions with two robot-assisted gait trainers (Lokomat and Lyra). The measured data from the sensors were analyzed by a custom-made gait event detection algorithm. An overall detection rate of 98.1% ± 5.2% for the Lokomat and 94.1% ± 6.8% for the Lyra was achieved. The mean type-1 error was 0.3% ± 1.2% for the Lokomat and 1.9% ± 4.3% for the Lyra. As a result, the setup provides promising results for further research and a technique that can enhance robot-assisted gait trainers by adding functional electrical stimulation to the rehabilitation process.

scholarly journals Gait Event Detection for Stroke Patients during Robot-Assisted Gait Training

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3399
Author(s):  
Andreas Schicketmueller ◽  
Juliane Lamprecht ◽  
Marc Hofmann ◽  
Michael Sailer ◽  
Georg Rose
Keyword(s):  
Electrical Stimulation ◽  
Functional Electrical Stimulation ◽  
Gait Training ◽  
Patient Specific ◽  
Stroke Patients ◽  
Robot Assisted ◽  
Rehabilitation Process ◽  
Type 1 Error ◽  
Movement Data ◽  
Measurement Units

Functional electrical stimulation and robot-assisted gait training are techniques which are used in a clinical routine to enhance the rehabilitation process of stroke patients. By combining these technologies, therapy effects could be further improved and the rehabilitation process can be supported. In order to combine these technologies, a novel algorithm was developed, which aims to extract gait events based on movement data recorded with inertial measurement units. In perspective, the extracted gait events can be used to trigger functional electrical stimulation during robot-assisted gait training. This approach offers the possibility of equipping a broad range of potential robot-assisted gait trainers with functional electrical stimulation. In particular, the aim of this study was to test the robustness of the previously developed algorithm in a clinical setting with patients who suffered a stroke. A total amount of N = 10 stroke patients participated in the study, with written consent. The patients were assigned to two different robot-assisted gait trainers (Lyra and Lokomat) according to their performance level, resulting in five recording sessions for each gait-trainer. A previously developed algorithm was applied and further optimized in order to extract the gait events. A mean detection rate across all patients of 95.8% ± 7.5% for the Lyra and 98.7% ± 2.6% for the Lokomat was achieved. The mean type 1 error across all patients was 1.0% ± 2.0% for the Lyra and 0.9% ± 2.3% for the Lokomat. As a result, the developed algorithm was robust against patient specific movements, and provided promising results for the further development of a technique that can detect gait events during robot-assisted gait training, with the future aim to trigger functional electrical stimulation.

A proposal for patient-tailored supervision of movement performance during end-effector-based robot-assisted rehabilitation of the upper extremities

2015 ◽  
Vol 60 (3) ◽  
Author(s):  
Michael Hennes ◽  
Kai Bollue ◽  
Henry Arenbeck ◽  
Catherine Disselhorst-Klug
Keyword(s):  
Experimental Tests ◽  
Inertial Measurement Units ◽  
Robot Assisted ◽  
End Effector ◽  
Rehabilitation Process ◽  
Inertial Measurement ◽  
Movement Performance ◽  
Movement Strategies ◽  
Rehabilitation System ◽  
Measurement Units

AbstractMillions of people worldwide suffer from stroke each year. One way to assist patients cost-effectively during their rehabilitation process is using end-effector-based robot-assisted rehabilitation. Such systems allow patients to use their own movement strategies to perform a movement task, which encourages them to do self-motivated training but also allow compensation movements if they have problems executing the movement tasks. Therefore, a patient supervision system was developed on the basis of inertial measurement units and a patient-tailored movement interpretation system. Very light and small inertial measurement units were developed to record the patients’ movements during a teaching phase in which the desired movement is shown to the patient by a physiotherapist. During a following exercise phase, the patient is training the previously shown movement alone with the help of an end-effector-based robot-assisted rehabilitation system, and the patient’s movement is recorded again. The data from the teaching and exercise phases are compared with each other and evaluated by using fuzzy logic tailored to each patient. Experimental tests with one healthy subject and one stroke patient showed the capability of the system to supervise patient movements during the robot-assisted end-effector-based rehabilitation.

scholarly journals Effect of Functional Electrical Stimulation of the Gluteus Medius during Gait in Patients following a Stroke

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Sota Araki ◽  
Masayuki Kawada ◽  
Takasuke Miyazaki ◽  
Yuki Nakai ◽  
Yasufumi Takeshita ◽  
...  
Keyword(s):  
Electrical Stimulation ◽  
Lower Limb ◽  
Stride Length ◽  
Gluteus Medius ◽  
Gait Velocity ◽  
Inertial Measurement Units ◽  
Stroke Patients ◽  
Inertial Measurement ◽  
Motion Range ◽  
Measurement Units

Many stroke patients rely on cane or ankle-foot orthosis during gait rehabilitation. The purpose of this study was to investigate the immediate effect of functional electrical stimulation (FES) to the gluteus medius (GMed) and tibialis anterior (TA) on gait performance in stroke patients, including those who needed assistive devices. Fourteen stroke patients were enrolled in this study (mean poststroke duration: 194.9 ± 189.6   d ; mean age: 72.8 ± 10.7   y ). Participants walked 14 m at a comfortable velocity with and without FES to the GMed and TA. After an adaptation period, lower-limb motion was measured using magnetic inertial measurement units attached to the pelvis and the lower limb of the affected side. Motion range of angle of the affected thigh and shank segments in the sagittal plane, motion range of the affected hip and knee extension-flexion angle, step time, and stride time were calculated from inertial measurement units during the middle ten walking strides. Gait velocity, cadence, and stride length were also calculated. These gait indicators, both with and without FES, were compared. Gait velocity was significantly faster with FES ( p = 0.035 ). Similarly, stride length and motion range of the shank of the affected side were significantly greater with FES (stride length: p = 0.018 ; motion range of the shank: p = 0.02 6). Meanwhile, cadence showed no significant difference ( p = 0.238 ) in gait with or without FES. Similarly, range of motion of the affected hip joint, knee joint, and thigh did not differ significantly depending on FES condition ( p = 0.115 ‐ 0.529 ). FES to the GMed and TA during gait produced an improvement in gait velocity, stride length, and motion range of the shank. Our results will allow therapists to use FES on stroke patients with varying conditions.

scholarly journals Gait event detection using inertial measurement units in people with transfemoral amputation: a comparative study

2019 ◽  
Vol 58 (3) ◽  
pp. 461-470 ◽  
Author(s):  
Emeline Simonetti ◽  
Coralie Villa ◽  
Joseph Bascou ◽  
Giuseppe Vannozzi ◽  
Elena Bergamini ◽  
...  
Keyword(s):  
Comparative Study ◽  
Event Detection ◽  
Inertial Measurement Units ◽  
Transfemoral Amputation ◽  
Inertial Measurement ◽  
Measurement Units

scholarly journals Template-Based Step Detection with Inertial Measurement Units

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4033 ◽  
Author(s):  
Laurent Oudre ◽  
Rémi Barrois-Müller ◽  
Thomas Moreau ◽  
Charles Truong ◽  
Aliénor Vienne-Jumeau ◽  
...  
Keyword(s):  
Healthy Subjects ◽  
Detection Algorithm ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Step Detection ◽  
Medical Context ◽  
Measurement Units ◽  
End Times

This article presents a method for step detection from accelerometer and gyrometer signals recorded with Inertial Measurement Units (IMUs). The principle of our step detection algorithm is to recognize the start and end times of the steps in the signal thanks to a predefined library of templates. The algorithm is tested on a database of 1020 recordings, composed of healthy subjects and patients with various neurological or orthopedic troubles. Simulations on more than 40,000 steps show that the template-based method achieves remarkable results with a 98% recall and a 98% precision. The method adapts well to pathological subjects and can be used in a medical context for robust step estimation and gait characterization.

scholarly journals Analysis of ergonomic and unergonomic human lifting behaviors by using Inertial Measurement Units

2017 ◽  
Vol 3 (1) ◽  
pp. 7-10 ◽  
Author(s):  
Jan Kuschan ◽  
Henning Schmidt ◽  
Jörg Krüger
Keyword(s):  
Quality Of Life ◽  
Machine Learning ◽  
Angular Velocity ◽  
Musculoskeletal System ◽  
Occupational Diseases ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Main Components

Abstract:This paper presents an analysis of two distinct human lifting movements regarding acceleration and angular velocity. For the first movement, the ergonomic one, the test persons produced the lifting power by squatting down, bending at the hips and knees only. Whereas performing the unergonomic one they bent forward lifting the box mainly with their backs. The measurements were taken by using a vest equipped with five Inertial Measurement Units (IMU) with 9 Dimensions of Freedom (DOF) each. In the following the IMU data captured for these two movements will be evaluated using statistics and visualized. It will also be discussed with respect to their suitability as features for further machine learning classifications. The reason for observing these movements is that occupational diseases of the musculoskeletal system lead to a reduction of the workers’ quality of life and extra costs for companies. Therefore, a vest, called CareJack, was designed to give the worker a real-time feedback about his ergonomic state while working. The CareJack is an approach to reduce the risk of spinal and back diseases. This paper will also present the idea behind it as well as its main components.

Sign in / Sign up

Export Citation Format

Share Document