A wrist sensor and algorithm to determine instantaneous walking cadence and speed in daily life walking

Self Cite

Wearable inertial devices have recently been used to evaluate spatiotemporal parameters of gait in daily life situations. Given the heterogeneity of gait patterns in children with cerebral palsy (CP), the sensor placement and analysis algorithm may influence the validity of the results. This study aimed at comparing the spatiotemporal measurement performances of three wearable configurations defined by different sensor positioning on the lower limbs: (1) shanks and thighs, (2) shanks, and (3) feet. The three configurations were selected based on their potential to be used in daily life for children with CP and typically developing (TD) controls. For each configuration, dedicated gait analysis algorithms were used to detect gait events and compute spatiotemporal parameters. Fifteen children with CP and 11 TD controls were included. Accuracy, precision, and agreement of the three configurations were determined in comparison with an optoelectronic system as a reference. The three configurations were comparable for the evaluation of TD children and children with a low level of disability (CP-GMFCS I) whereas the shank-and-thigh-based configuration was more robust regarding children with a higher level of disability (CP-GMFCS II–III).

Section: Discussionmentioning

confidence: 99%

What is the Best Configuration of Wearable Sensors to Measure Spatiotemporal Gait Parameters in Children with Cerebral Palsy?

Carcreff

Gerber

Paraschiv-Ionescu

et al. 2018

Self Cite

“…The algorithm was not validated for slow walking and achieved poor results for running segments of data. The study in [ 14 ] used a wrist worn accelerometer to estimate the walking cadence and speed in daily life walking in several environments. The study in [ 15 ] proposed the use of some particular points in the acceleration time series in order to detect steps at normal speeds.…”

Section: Related Workmentioning

confidence: 99%

Detecting Steps Walking at very Low Speeds Combining Outlier Detection, Transition Matrices and Autoencoders from Acceleration Patterns

Muñoz-Organero

Blázquez

2017

In this paper, we develop and validate a new algorithm to detect steps while walking at speeds between 30 and 40 steps per minute based on the data sensed from a single tri-axial accelerometer. The algorithm concatenates three consecutive phases. First, an outlier detection is performed on the sensed data based on the Mahalanobis distance to pre-detect candidate points in the acceleration time series that may contain a ground contact segment of data while walking. Second, the acceleration segment around the pre-detected point is used to calculate the transition matrix in order to capture the time dependencies. Finally, autoencoders, trained with data segments containing ground contact transition matrices from acceleration series from labeled steps are used to reconstruct the computed transition matrices at each pre-detected point. A similarity index is used to assess if the pre-selected point contains a true step in the 30–40 steps per minute speed range. Our experimental results, based on a database from three different participants performing similar activities to the target one, are able to achieve a recall = 0.88 with precision = 0.50 improving the results when directly applying the autoencoders to acceleration patterns (recall = 0.77 with precision = 0.50).

“…Single body segment mounted IMUs (e.g., wrist or pelvis) are limited in calculation of certain STGPs such as number of steps, step cadence, or step distance which may not be adequate for clinical applications [ 26 , 27 ]. Incorporating IMUs on additional body segments (e.g., foot, shank, thigh, pelvis, or trunk) may provide access to additional gait metrics and improve gait characteristic predictions, especially for patient populations with pathologic movement characteristics [ 26 , 28 , 29 , 30 ]. Carcreff et al demonstrated that IMUs placed on the shank and thigh yielded more accurate predictions of stride time, length, and velocity compared to feet mounted IMUs for children with cerebral palsy, particularly for those patients with increased disability [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Gait Parameter Prediction for OA and TKA Patients Wearing IMU Sensors

Renani

Myers

Zandie

et al. 2020

Quantitative assessments of patient movement quality in osteoarthritis (OA), specifically spatiotemporal gait parameters (STGPs), can provide in-depth insight into gait patterns, activity types, and changes in mobility after total knee arthroplasty (TKA). A study was conducted to benchmark the ability of multiple deep neural network (DNN) architectures to predict 12 STGPs from inertial measurement unit (IMU) data and to identify an optimal sensor combination, which has yet to be studied for OA and TKA subjects. DNNs were trained using movement data from 29 subjects, walking at slow, normal, and fast paces and evaluated with cross-fold validation over the subjects. Optimal sensor locations were determined by comparing prediction accuracy with 15 IMU configurations (pelvis, thigh, shank, and feet). Percent error across the 12 STGPs ranged from 2.1% (stride time) to 73.7% (toe-out angle) and overall was more accurate in temporal parameters than spatial parameters. The most and least accurate sensor combinations were feet-thighs and singular pelvis, respectively. DNNs showed promising results in predicting STGPs for OA and TKA subjects based on signals from IMU sensors and overcomes the dependency on sensor locations that can hinder the design of patient monitoring systems for clinical application.