IMU-based Spectrogram Approach with Deep Convolutional Neural Networks for Gait Classification

With an ageing society comes the increased prevalence of gait disorders. The restriction of mobility leads to a considerable reduction in the quality of life, because associated falls increase morbidity and mortality. Consideration of gait analysis data often alters surgical recommendations. For that reason, the early and systematic diagnostic treatment of gait disorders can spare a lot of suffering. As modern gait analysis systems are, in most cases, still very costly, many patients are not privileged enough to have access to comparable therapies. Low-cost systems such as inertial measurement units (IMUs) still pose major challenges, but offer possibilities for automatic real-time motion analysis. In this paper, we present a new approach to reliably detect human gait phases, using IMUs and machine learning methods. This approach should form the foundation of a new medical device to be used for gait analysis. A model is presented combining deep 2D-convolutional and LSTM networks to perform a classification task; it predicts the current gait phase with an accuracy of over 92% on an unseen subject, differentiating between five different phases. In the course of the paper, different approaches to optimize the performance of the model are presented and evaluated.

Section: Introductionmentioning

confidence: 99%

Deep Convolutional and LSTM Networks on Multi-Channel Time Series Data for Gait Phase Recognition

Kreuzer

Münz

2021

“…The identification of gait events in human locomotion based on inertial measurement units (IMU) data is currently a vivid research area [11,12]. Several methods are proposed using the accelerometer data [13,14] or the gyrometer data [14,15] based on threshold [16] or signal pattern identification [17].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Trotting Stance Detection Methods from an Inertial Measurement Unit Mounted on the Horse’s Limb

Sapone

Martin

Mansour

et al. 2020

The development of on-board sensors, such as inertial measurement units (IMU), has made it possible to develop new methods for analyzing horse locomotion to detect lameness. The detection of spatiotemporal events is one of the keystones in the analysis of horse locomotion. This study assesses the performance of four methods for detecting Foot on and Foot off events. They were developed from an IMU positioned on the canon bone of eight horses during trotting recording on a treadmill and compared to a standard gold method based on motion capture. These methods are based on accelerometer and gyroscope data and use either thresholding or wavelets to detect stride events. The two methods developed from gyroscopic data showed more precision than those developed from accelerometric data with a bias less than 0.6% of stride duration for Foot on and 0.1% of stride duration for Foot off. The gyroscope is less impacted by the different patterns of strides, specific to each horse. To conclude, methods using the gyroscope present the potential of further developments to investigate the effects of different gait paces and ground types in the analysis of horse locomotion.

“…This has allowed for the establishment of an exact ranking among different solutions for continuous mobility monitoring. Although previous studies have assessed the effect of different device locations [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ], the effect of a variety of fixation methods had not yet been explored. Thus, we have developed and applied a novel quantitative approach to allow these criteria to be explicitly identified and ranked.…”

Section: Discussionmentioning

confidence: 99%

“…A variety of data processing algorithms to estimate digital mobility outcomes (DMOs, e.g., walking speed, cadence, etc. ), from either a single or multiple devices, have been proposed and validated [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ], and the effect of different device locations has also been assessed [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ]. Nonetheless, algorithms and associated wearable devices are still far from widespread adoption outside of research labs due to several other limiting factors, such as human factors, wearability, usability, and data capture.…”

Section: Introductionmentioning

confidence: 99%

An Objective Methodology for the Selection of a Device for Continuous Mobility Assessment

Bonci

Keogh

Din

et al. 2020

Continuous monitoring by wearable technology is ideal for quantifying mobility outcomes in “real-world” conditions. Concurrent factors such as validity, usability, and acceptability of such technology need to be accounted for when choosing a monitoring device. This study proposes a bespoke methodology focused on defining a decision matrix to allow for effective decision making. A weighting system based on responses (n = 69) from a purpose-built questionnaire circulated within the IMI Mobilise-D consortium and its external collaborators was established, accounting for respondents’ background and level of expertise in using wearables in clinical practice. Four domains (concurrent validity, CV; human factors, HF; wearability and usability, WU; and data capture process, CP), associated evaluation criteria, and scores were established through literature research and group discussions. While the CV was perceived as the most relevant domain (37%), the others were also considered highly relevant (WU: 30%, HF: 17%, CP: 16%). Respondents (~90%) preferred a hidden fixation and identified the lower back as an ideal sensor location for mobility outcomes. Overall, this study provides a novel, holistic, objective, as well as a standardized approach accounting for complementary aspects that should be considered by professionals and researchers when selecting a solution for continuous mobility monitoring.