Foot worn inertial sensors for gait assessment and rehabilitation based on motorized shoes

Wearable devices equipped with inertial sensors enable objective gait assessment for persons with multiple sclerosis (MS), with potential use in ambulatory care or home and community-based assessments. However, gait data collected in non-controlled settings are often fragmented and may not provide enough information for reliable measures. This paper evaluates a novel approach to (1) determine the effects of the length of the walking task on the reliability of calculated measures and (2) identify digital biomarkers for gait assessments from fragmented data. Thirty-seven participants (37) diagnosed with relapsing-remitting MS (EDSS range 0 to 4.5) executed two trials, walking 20 m each, with inertial sensors attached to their right and left shanks. Gait events were identified from the medio-lateral angular velocity, and short bouts of gait data were extracted from each trial, with lengths varying from 3 to 9 gait cycles. Intraclass correlation coefficients (ICCs) evaluate the degree of agreement between the two trials of each participant, according to the number of gait cycles included in the analysis. Results show that short bouts of gait data, including at least six gait cycles of bilateral data, can provide reliable gait measurements for persons with MS, opening new perspectives for gait assessment using fragmented data (e.g., wearable devices, community assessments). Stride time variability and asymmetry, as well as stride velocity variability and asymmetry, should be further explored as digital biomarkers to support the monitoring of symptoms of persons with neurological diseases.

Download Full-text

Short bouts of gait data and inertial sensors can provide reliable measures of spatiotemporal gait parameters from bilateral gait data of participants with multiple sclerosis

10.21203/rs.3.rs-27071/v1 ◽

2020 ◽

Author(s):

Lilian Genaro Motti Ader ◽

Barry R. Greene ◽

Killian McManus ◽

Niall Tubridy ◽

Brian Caulfield

Keyword(s):

Multiple Sclerosis ◽

Inertial Sensors ◽

Intraclass Correlation ◽

Full Length ◽

Time Variability ◽

Stride Time ◽

Good Reliability ◽

Time Asymmetry ◽

Gait Assessment ◽

Gait Parameters

Abstract Background:Wearable devices equipped with inertial sensors enable objective gait assessment for persons with multiple sclerosis (MS), with potential use in ambulatory care or home and community-based assessments. However, gait data collected in non-controlled settings is often fragmented and may not provide enough information for reliable measures. We evaluate a novel approach, extracting pre-defined numbers of gait cycles from the full length of a walking task, and their effects on the reliability of spatiotemporal gait parameters.Methods:The present study evaluates intra-session reliability of spatiotemporal gait parameters for short bouts of gait data extracted from the full length of the walking tasks to 1) determine the effects of the length of the walking task on the reliability of calculated measures and 2) identify spatiotemporal gait parameters that can provide reliable measures for gait assessments and reference data in different settings.Thirty-seven participants (37) diagnosed with relapsing-remitting MS (EDSS rage 0 to 4.5) executed two trials, walking 20m each, with inertial sensors attached to their right and left shanks. Previously published algorithms were applied to identify gait events from the medio-lateral angular velocity. Short bouts of gait data were extracted from each trial, with lengths varying from 3 to 9 gait cycles. Twenty-one measures of spatiotemporal gait parameters were calculated. Intraclass correlation coefficients (ICCs) were calculated to evaluate how the degree of agreement between the two trials of each participant varied with the number of gait cycles included in the analysis.Results:Spatiotemporal gait parameters calculated as the mean across included gait cycles reach excellent reliability from three gait cycles. Stride time variability and asymmetry, as well as stride velocity variability and asymmetry, reach good reliability from six gait cycles and should be further explored for persons with MS, while stride time asymmetry and step time asymmetry do not seem to provide reliable measures and should be reported carefully.Conclusion:Short bouts of gait data, including at least six gait cycles of bilateral data, can provide reliable gait measurements for persons with MS, opening new perspectives for gait assessment using wearable devices in non-controlled environments, to support monitoring of symptoms of persons with neurological diseases.Trial registrationNot applicable.

Download Full-text

Children with cerebral palsy who undergo gait assessment before surgery are less likely to need additional surgery later

PsycEXTRA Dataset ◽

10.1037/e554812010-016 ◽

2010 ◽

Keyword(s):

Cerebral Palsy ◽

Gait Assessment ◽

Additional Surgery ◽

Children With Cerebral Palsy

Download Full-text

Analysis of Raw GNSS Measurements Derived Navigation Solutions from Mobile Devices with Inertial Sensors

Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019) ◽

10.33012/2019.17070 ◽

2019 ◽

Cited By ~ 1

Author(s):

Dong-Kyeong Lee ◽

Matthias Petit ◽

Damian Miralles ◽

Sherman Lo ◽

Dennis Akos

Keyword(s):

Mobile Devices ◽

Inertial Sensors ◽

Gnss Measurements

Download Full-text

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.17.3dmp-a17 ◽

2020 ◽

Vol 2020 (17) ◽

pp. 2-1-2-6

Author(s):

Shih-Wei Sun ◽

Ting-Chen Mou ◽

Pao-Chi Chang

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Network Structure ◽

Inertial Sensors ◽

Body Movement ◽

The Body ◽

Body Parts ◽

Multimodal Sensing ◽

Multiple Devices ◽

Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

Download Full-text

A Preliminary Framework and Feasibility Study for Spacecraft Disturbance Source Detection and Isolation with Distributed Inertial Sensors

AIAA Scitech 2021 Forum ◽

10.2514/6.2021-1867 ◽

2021 ◽

Author(s):

Adam Augustyniak ◽

David J. Hanley ◽

Timothy W. Bretl ◽

Neil J. Hejmanowski ◽

David L. Carroll

Keyword(s):

Feasibility Study ◽

Inertial Sensors ◽

Source Detection ◽

Disturbance Source

Download Full-text

Gait Assessment for Neurologically Impaired Patients

Physical Therapy ◽

10.1093/ptj/66.10.1530 ◽

1986 ◽

Vol 66 (10) ◽

pp. 1530-1539 ◽

Cited By ~ 287

Author(s):

Maureen K. Holden ◽

Kathleen M. Gill ◽

Marie R. Magliozzi

Keyword(s):

Neurologically Impaired ◽

Gait Assessment ◽

Neurologically Impaired Patients

Download Full-text

Wearable Technologies in Field Hockey Competitions: A Scoping Review

Sensors ◽

10.3390/s21155242 ◽

2021 ◽

Vol 21 (15) ◽

pp. 5242

Author(s):

Jolene Ziyuan Lim ◽

Alexiaa Sim ◽

Pui Wah Kong

Keyword(s):

Heart Rate ◽

Inertial Sensors ◽

Wearable Devices ◽

Field Hockey ◽

Heart Rate Monitors ◽

Search Terms ◽

Physiological Demands ◽

And Performance ◽

Future Work ◽

Skin Temperatures

The aim of this review is to investigate the common wearable devices currently used in field hockey competitions, and to understand the hockey-specific parameters these devices measure. A systematic search was conducted by using three electronic databases and search terms that included field hockey, wearables, accelerometers, inertial sensors, global positioning system (GPS), heart rate monitors, load, performance analysis, player activity profiles, and competitions from the earliest record. The review included 39 studies that used wearable devices during competitions. GPS units were found to be the most common wearable in elite field hockey competitions, followed by heart rate monitors. Wearables in field hockey are mostly used to measure player activity profiles and physiological demands. Inconsistencies in sampling rates and performance bands make comparisons between studies challenging. Nonetheless, this review demonstrated that wearable devices are being used for various applications in field hockey. Researchers, engineers, coaches, and sport scientists can consider using GPS units of higher sampling rates, as well as including additional variables such as skin temperatures and injury associations, to provide a more thorough evaluation of players’ physical and physiological performances. Future work should include goalkeepers and non-elite players who are less studied in the current literature.

Download Full-text