scholarly journals Real-Time Auditory Feedback–Induced Adaptation to Walking Among Seniors Using the Heel2Toe Sensor: Proof-of-Concept Study (Preprint)

2019 ◽  
Author(s):  
Kedar K.V. Mate ◽  
Ahmed Abou-Sharkh ◽  
José A. Morais ◽  
Nancy E. Mayo
Keyword(s):  
Real Time ◽  
Auditory Feedback ◽  
Visual Cues ◽  
Gait Training ◽  
Gait Pattern ◽  
Arm Swing ◽  
Simple Strategy ◽  
Minute Period ◽  
Gait Parameters ◽  
Strategy Changes

BACKGROUND Evidence shows that gait training in older adults is effective in improving the gait pattern, but the effects abate with cessation of training. During gait training, therapists use a number of verbal and visual cues to place the heel first when stepping. This simple strategy changes posture from stooped to upright, lengthens the stride, stimulates pelvic and trunk rotation, and facilitates arm swing. These principles guided the development of the Heel2Toe sensor that provides real-time auditory feedback for each good step, in which the heel strikes first. OBJECTIVE This feasibility study aimed (1) to contribute evidence toward the feasibility and efficacy potential for home use of the Heel2Toe sensor that provides real-time feedback and (2) to estimate changes in gait parameters after five training sessions using the sensor. METHODS A pre-post study included 5 training sessions over 2 weeks in the community on a purposive sample of six seniors. Proportion of good steps, angular velocity (AV) at each step, and cadence over a 2- minute period were assessed as was usability and experience. RESULTS All gait parameters, proportion of good steps, AV, and duration of walking bouts improved. The coefficient of variation of AV decreased, indicating consistency of stepping. CONCLUSIONS Efficacy potential and feasibility of the Heel2Toe sensor were demonstrated.

scholarly journals Real-Time Auditory Feedback–Induced Adaptation to Walking Among Seniors Using the Heel2Toe Sensor: Proof-of-Concept Study

10.2196/13889 ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. e13889
Author(s):  
Kedar KV Mate ◽  
Ahmed Abou-Sharkh ◽  
José A Morais ◽  
Nancy E Mayo
Keyword(s):  
Real Time ◽  
Auditory Feedback ◽  
Visual Cues ◽  
Gait Training ◽  
Gait Pattern ◽  
Arm Swing ◽  
Simple Strategy ◽  
Minute Period ◽  
Gait Parameters ◽  
Strategy Changes

Background Evidence shows that gait training in older adults is effective in improving the gait pattern, but the effects abate with cessation of training. During gait training, therapists use a number of verbal and visual cues to place the heel first when stepping. This simple strategy changes posture from stooped to upright, lengthens the stride, stimulates pelvic and trunk rotation, and facilitates arm swing. These principles guided the development of the Heel2Toe sensor that provides real-time auditory feedback for each good step, in which the heel strikes first. Objective This feasibility study aimed (1) to contribute evidence toward the feasibility and efficacy potential for home use of the Heel2Toe sensor that provides real-time feedback and (2) to estimate changes in gait parameters after five training sessions using the sensor. Methods A pre-post study included 5 training sessions over 2 weeks in the community on a purposive sample of six seniors. Proportion of good steps, angular velocity (AV) at each step, and cadence over a 2- minute period were assessed as was usability and experience. Results All gait parameters, proportion of good steps, AV, and duration of walking bouts improved. The coefficient of variation of AV decreased, indicating consistency of stepping. Conclusions Efficacy potential and feasibility of the Heel2Toe sensor were demonstrated.

EXPLOITING VISUAL CUES FOR LEARNING GAIT PATTERNS ASSOCIATED WITH NEUROLOGICAL DISORDERS

2017 ◽  
Vol 79 (3) ◽  
Author(s):  
Kuhelee Roy ◽  
Geelapaturu Subrahmanya Venkata Radha Krish Rao ◽  
Savarimuthu, Margret Anouncia
Keyword(s):  
Early Diagnosis ◽  
Real Time ◽  
Neurological Disorders ◽  
Visual Cues ◽  
Gait Pattern ◽  
Shape Features ◽  
Gait Patterns ◽  
Normal Gait ◽  
Abnormal Gait ◽  
Gait Abnormalities

Records of cases involving neurological disorders often exhibit abnormalities in the gait pattern of an individual. As mentioned in various articles, the causes of various gait disorders can be attributed to neurological disorders. Hence analysis of gait abnormalities can be a key to predict the type of neurological disorders as a part of early diagnosis. A number of sensor-based measurements have aided towards quantifying the degree of abnormalities in a gait pattern. A shape oriented motion based approach has been proposed in this paper to envisage the task of classifying an abnormal gait pattern into one of the five types of gait viz. Parkinsonian, Scissor, Spastic, Steppage and Normal gait. The motion and shape features for two cases viz. right-leg-front and left-leg-front will be taken into account. Experimental results of application on real-time videos suggest the reliability of the proposed method.

scholarly journals GaitVision: Real-Time Extraction of Gait Parameters Using Residual Attention Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Mohammad Farukh Hashmi ◽  
B. Kiran Kumar Ashish ◽  
Prabhu Chaitanya ◽  
Avinash Keskar ◽  
Sinan Q. Salih ◽  
...  
Keyword(s):  
Real Time ◽  
Gait Pattern ◽  
Foot Pressure ◽  
Gait Patterns ◽  
Softmax Classifier ◽  
Gait Parameters ◽  
Unique Identity ◽  
Access Authentication ◽  
Deep Convolution Network ◽  
Hierarchical Features

Gait walking patterns are one of the key research topics in natural biometrics. The temporal information of the unique gait sequence of a person is preserved and used as a powerful data for access. Often there is a dive into the flexibility of gait sequence due to unstructured and unnecessary sequences that tail off the necessary sequence constraints. The authors in this work present a novel perspective, which extracts useful gait parameters regarded as independent frames and patterns. These patterns and parameters mark as unique signature for each subject in access authentication. This information extracted learns to identify the patterns associated to form a unique gait signature for each person based on their style, foot pressure, angle of walking, angle of bending, acceleration of walk, and step-by-step distance. These parameters form a unique pattern to plot under unique identity for access authorization. This sanitized data of patterns is further passed to a residual deep convolution network that automatically extracts the hierarchical features of gait pattern signatures. The end layer comprises of a Softmax classifier to classify the final prediction of the subject identity. This state-of-the-art work creates a gait-based access authentication that can be used in highly secured premises. This work was specially designed for Defence Department premises authentication. The authors have achieved an accuracy of 90 % ± 1.3 % in real time. This paper mainly focuses on the assessment of the crucial features of gait patterns and analysis of gait patterns research.

Effects of repetitive intensive arm swing indirect gait training on vasti and hamstring muscle activity and gait performance in children with cerebral palsy

2020 ◽  
Vol 27 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Yon Ju Sim ◽  
Dong Ryul Lee ◽  
Chung Hwi Yi ◽  
Heon Seock Cynn
Keyword(s):  
Cerebral Palsy ◽  
Muscle Activity ◽  
Gait Training ◽  
Lower Limbs ◽  
Clinical Test ◽  
Arm Swing ◽  
Gait Parameters ◽  
Children With Cerebral Palsy ◽  
Before And After ◽  
Training Programmes

Background/aims Both upper and lower limbs interact through neural coupling. Such interconnection leads to rhythmic interlimb coordination, which affects the central pattern generator for the lower limbs. The aim of this study was to investigate the effects of repetitive intensive arm swing indirect gait training on muscle activity and gait parameters in children with cerebral palsy. Methods A total of 9 children with cerebral palsy were recruited for 20 sessions of repetitive intensive arm swing indirect gait training. They were tested before and after completion of this training using surface electromyography, spatiotemporal gait parameters assessments and clinical tests. A paired t-test was used to investigate differences in participants' vasti and hamstring activity, spatiotemporal gait parameters, and clinical test results before and after the training. Results Participants' vasti muscle activity increased significantly after the repetitive intensive arm swing indirect gait training, but there was no significant change in their hamstring muscles. However, spatiotemporal gait parameters and clinical motor function improved significantly. Conclusions Repetitive intensive arm swing indirect gait training may be suitable as an effective exercise in gait training programmes for children with cerebral palsy.

scholarly journals Real-Time Foot Clearance Biofeedback to Assist Gait Rehabilitation Following Stroke: a Randomised Controlled Trial Protocol

2018 ◽  
Author(s):  
Rezaul Begg ◽  
Mary Galea ◽  
Lisa James ◽  
Tony Sparrow ◽  
Pazit Levinger ◽  
...  
Keyword(s):  
Real Time ◽  
Controlled Trial ◽  
Intervention Group ◽  
Training Session ◽  
Sensory Information ◽  
Gait Training ◽  
Gait Pattern ◽  
Falls Prevention ◽  
Visual Biofeedback ◽  
The Impact

Abstract Background: The risk of falling is significantly higher in people with chronic stroke and it is, therefore, important to design interventions to improve mobility and decrease falls risk. Minimum Toe Clearance (MTC) is the key gait cycle event for predicting tripping-falls because it occurs mid-swing during the walking cycle where forward velocity of the foot is maximum. High forward velocity coupled with low MTC increases the probability of unanticipated foot-ground contacts. Training procedures to increase toe-ground clearance (MTC) have potential, therefore, as a falls prevention intervention. The aim of this project is to determine whether augmented sensory information via real-time visual biofeedback during gait training can increase MTC. Methods: Participants will be over 18 years, have sustained a single stroke (ischaemic or hemorrhagic) at least 6 months previously, able to walk 50 metres independently and capable of informed consent. Using a secure web-based application (REDCap) 150 participants will be randomly assigned to either no-feedback (Control) or feedback (Experimental) groups, all will receive 10 sessions of treadmill training for up to 10 minutes at a self-selected speed over five to six weeks. The intervention group will receive real-time, visual biofeedback of MTC during training and will be asked to modify their gait pattern to match a required “target” criterion. Biofeedback is continuous for the first six sessions then progressively reduced (faded) across the remaining four sessions. Control participants will walk on the treadmill without biofeedback. Gait assessments are conducted at baseline, immediately following the final training session and then during follow-up, at 1, 3 and 6 months. The primary outcome measure is MTC. Monthly falls calendars will also be collected for 12 months from enrolment. Discussion: This project will evaluate the impact of augmented sensory information, via visually presented biofeedback, for improving gait function in people with stroke. This has implications for the rehabilitation of gait disorders following stroke and may have the potential to reduce falls in this population.

Comparison of gait parameters of pregnant women with non-pregnant women

2020 ◽  
Vol 14 (4) ◽  
pp. 1-7
Author(s):  
Bosede Abidemi Tella ◽  
Olufunke Adewumi Ajiboye ◽  
Daniel Olufemi Odebiyi ◽  
Oluwatoyin Mauren Johnson ◽  
Rose Ihuoma Anorlu
Keyword(s):  
Pregnant Women ◽  
Body Shape ◽  
Step Length ◽  
Gait Training ◽  
Gait Pattern ◽  
Gait Velocity ◽  
Gait Parameters ◽  
Significant Difference ◽  
Base Of Support ◽  
Apparently Healthy

Background/Aims The changes in body weight, body shape and hormones of pregnant women alter the posture and gait pattern of these individuals compared to non-pregnant women. The purpose of this study was to determine the effect of pregnancy on selected gait parameters by evaluating footprints at the second and third trimesters of pregnancy and comparing with apparently healthy, non-pregnant women. Methods A total of 40 consenting women (20 pregnant and 20 non-pregnant age-matched women) aged 22–35 years old (mean 28.25±0.68 years) participated in this study. Footprints were obtained from each participant and selected gait parameters were computed from the footprints. Paired t-tests and independent t-tests were used to compare the variables at P<0.05. Results There was a significant difference in the gait parameters measured between the pregnant and non-pregnant women: gait velocity (P=0.001), cadence (P=0.001), right foot angle (P=0.001), left foot angle (P=0.002), base of support (P=0.001), right step length (P=0.001), left step length (P=0.001). However, there was no significant difference in the gait parameters measured between the pregnant women in their second and third trimesters. Conclusions Pregnancy is associated with significant changes in most gait variables compared to non-pregnant women, although no significant change was observed between the second and third trimesters of pregnancy. The inclusion of gait training during antenatal care may help reduce the effect on the musculoskeletal system.

scholarly journals Effect of assist-as-needed robotic gait training on the gait pattern post stroke: a randomized controlled trial

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
J. F. Alingh ◽  
B. M. Fleerkotte ◽  
B. E. Groen ◽  
J. S. Rietman ◽  
V. Weerdesteyn ◽  
...  
Keyword(s):  
Randomized Controlled Trial ◽  
Knee Flexion ◽  
Controlled Trial ◽  
Gait Training ◽  
Gait Pattern ◽  
Robotic Training ◽  
Post Stroke ◽  
Randomized Controlled ◽  
Gait Parameters ◽  
Functional Gait

Abstract Background Regaining gait capacity is an important rehabilitation goal post stroke. Compared to clinically available robotic gait trainers, robots with an assist-as-needed approach and multiple degrees of freedom (AANmDOF) are expected to support motor learning, and might improve the post-stroke gait pattern. However, their benefits compared to conventional gait training have not yet been shown in a randomized controlled trial (RCT). The aim of this two-center, assessor-blinded, RCT was to compare the effect of AANmDOF robotic to conventional training on the gait pattern and functional gait tasks during post-stroke inpatient rehabilitation. Methods Thirty-four participants with unilateral, supratentorial stroke were enrolled (< 10 weeks post onset, Functional Ambulation Categories 3–5) and randomly assigned to six weeks of AANmDOF robotic (combination of training in LOPES-II and conventional gait training) or conventional gait training (30 min, 3–5 times a week), focused on pre-defined training goals. Randomization and allocation to training group were carried out by an independent researcher. External mechanical work (WEXT), spatiotemporal gait parameters, gait kinematics related to pre-defined training goals, and functional gait tasks were assessed before training (T0), after training (T1), and at 4-months follow-up (T2). Results Two participants, one in each group, were excluded from analysis because of discontinued participation after T0, leaving 32 participants (AANmDOF robotic n = 17; conventional n = 15) for intention-to-treat analysis. In both groups, WEXT had decreased at T1 and had become similar to baseline at T2, while gait speed had increased at both assessments. In both groups, most spatiotemporal gait parameters and functional gait tasks had improved at T1 and T2. Except for step width (T0–T1) and paretic step length (T0–T2), there were no significant group differences at T1 or T2 compared to T0. In participants with a pre-defined goal aimed at foot clearance, paretic knee flexion improved more in the AANmDOF robotic group compared to the conventional group (T0–T2). Conclusions Generally, AANmDOF robotic training was not superior to conventional training for improving gait pattern in subacute stroke survivors. Both groups improved their mechanical gait efficiency. Yet, AANmDOF robotic training might be more effective to improve specific post-stroke gait abnormalities such as reduced knee flexion during swing. Trial registration Registry number Netherlands Trial Register (www.trialregister.nl): NTR5060. Registered 13 February 2015.

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