Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Mobile Sensor Systems and Machine Learning for Gait and Biomechanical Motion Analysis

Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 22705

Share This Special Issue

Special Issue Editor

Dr. Felix Kluge

E-Mail Website
Guest Editor

FAU Erlangen Department of Computer Science, Carl-Thiersch-Straße 2b, 91052 Erlangen, Germany
Interests: Digital health; Mobile health; Machine Learning; Wearable sensors; Biomechanics; Human gait analysis; Movement Analysis; Ambulatory motion tracking, Gait parameters

Special Issue Information

Dear Colleagues,

The advent of wearable sensor systems for unobtrusive mobile gait and movement analysis presents a great potential for applications in the laboratory and real-world settings. To realize this potential, state-of-the-art signal processing and machine learning tools need to be developed. This will allow us to gain new insights into physiological and pathological mechanisms, which in turn will enable digital mobility parameters characterizing the health state and representing disease-related symptoms to be derived. Furthermore, methods need to be designed to efficiently deal with the large amount of data generated and to provide feedback to patients, researchers, and physicians.

This Special Issue will push forward current frontiers of human movement analysis by focusing on the development and application of validated novel sensor systems and analysis approaches, especially incorporating state-of-the-art signal processing and machine learning methods.

Topics of interest include but are not limited to the following:

Wearable sensor systems for quantitative gait and movement analysis;
Real-world and free-living data processing;
Machine-learning-based approaches for deriving gait and movement parameters;
Exploration of deep learning capabilities to learn representations to model disease progression;
Digital biomarker development;
Machine learning in biomechanical simulations.

Dr. Felix Kluge
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Gait analysis
Movement analysis
Biomechanics
Mobile sensors
Wearables
Machine learning
Deep learning
Simulation
Inertial magnetic measurement units

Published Papers (6 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

22 pages, 1058 KiB

Open AccessArticle

A Deep Learning Approach for Foot Trajectory Estimation in Gait Analysis Using Inertial Sensors

by Vânia Guimarães, Inês Sousa and Miguel Velhote Correia

Sensors 2021, 21(22), 7517; https://doi.org/10.3390/s21227517 - 12 Nov 2021

Cited by 12 | Viewed by 3281

Abstract

Gait performance is an important marker of motor and cognitive decline in older adults. An instrumented gait analysis resorting to inertial sensors allows the complete evaluation of spatiotemporal gait parameters, offering an alternative to laboratory-based assessments. To estimate gait parameters, foot trajectories are typically obtained by integrating acceleration two times. However, to deal with cumulative integration errors, additional error handling strategies are required. In this study, we propose an alternative approach based on a deep recurrent neural network to estimate heel and toe trajectories. We propose a coordinate frame transformation for stride trajectories that eliminates the dependency from previous strides and external inputs. Predicted trajectories are used to estimate an extensive set of spatiotemporal gait parameters. We evaluate the results in a dataset comprising foot-worn inertial sensor data acquired from a group of young adults, using an optical motion capture system as a reference. Heel and toe trajectories are predicted with low errors, in line with reference trajectories. A good agreement is also achieved between the reference and estimated gait parameters, in particular when turning strides are excluded from the analysis. The performance of the method is shown to be robust to imperfect sensor-foot alignment conditions. Full article

(This article belongs to the Special Issue Mobile Sensor Systems and Machine Learning for Gait and Biomechanical Motion Analysis)

► Show Figures

Figure 1

17 pages, 2328 KiB

Open AccessArticle

Joint Torque Prediction via Hybrid Neuromusculoskeletal Modelling during Gait Using Statistical Ground Reaction Estimates: An Exploratory Study

by Shui Kan Lam and Ivan Vujaklija

Sensors 2021, 21(19), 6597; https://doi.org/10.3390/s21196597 - 2 Oct 2021

Cited by 3 | Viewed by 2813

Abstract

Joint torques of lower extremity are important clinical indicators of gait capability. This parameter can be quantified via hybrid neuromusculoskeletal modelling that combines electromyography-driven modelling and static optimisation. The simulations rely on kinematics and external force measurements, for example, ground reaction forces (GRF) and the corresponding centres of pressure (COP), which are conventionally acquired using force plates. This bulky equipment, however, hinders gait analysis in real-world environments. While this portability issue could potentially be solved by estimating the parameters through machine learning, the effect of the estimation errors on joint torque prediction with biomechanical models remains to be investigated. This study first estimated GRF and COP through feedforward artificial neural networks, and then leveraged them to predict lower-limb sagittal joint torques via (i) inverse dynamics and (ii) hybrid modelling. The approach was evaluated on five healthy subjects, individually. The predicted torques were validated with the measured torques, showing that hip was the most sensitive whereas ankle was the most resistive to the GRF/COP estimates for both models, with average metrics values being 0.70 <

R^{2}

< 0.97 and 0.069 < RMSE < 0.15 (Nm/kg). This study demonstrated the feasibility of torque prediction based on personalised (neuro)musculoskeletal modelling using statistical ground reaction estimates, thus providing insights into potential real-world mobile joint torque quantification. Full article

(This article belongs to the Special Issue Mobile Sensor Systems and Machine Learning for Gait and Biomechanical Motion Analysis)

► Show Figures

Figure 1

28 pages, 10564 KiB

Open AccessArticle

An Inertial Sensor-Based Gait Analysis Pipeline for the Assessment of Real-World Stair Ambulation Parameters

by Nils Roth, Arne Küderle, Dominik Prossel, Heiko Gassner, Bjoern M. Eskofier and Felix Kluge

Sensors 2021, 21(19), 6559; https://doi.org/10.3390/s21196559 - 30 Sep 2021

Cited by 7 | Viewed by 3637

Abstract

Climbing stairs is a fundamental part of daily life, adding additional demands on the postural control system compared to level walking. Although real-world gait analysis studies likely contain stair ambulation sequences, algorithms dedicated to the analysis of such activities are still missing. Therefore, we propose a new gait analysis pipeline for foot-worn inertial sensors, which can segment, parametrize, and classify strides from continuous gait sequences that include level walking, stair ascending, and stair descending. For segmentation, an existing approach based on the hidden Markov model and a feature-based gait event detection were extended, reaching an average segmentation F1 score of 98.5% and gait event timing errors below ±10

m

s

for all conditions. Stride types were classified with an accuracy of 98.2% using spatial features derived from a Kalman filter-based trajectory reconstruction. The evaluation was performed on a dataset of 20 healthy participants walking on three different staircases at different speeds. The entire pipeline was additionally validated end-to-end on an independent dataset of 13 Parkinson’s disease patients. The presented work aims to extend real-world gait analysis by including stair ambulation parameters in order to gain new insights into mobility impairments that can be linked to clinically relevant conditions such as a patient’s fall risk and disease state or progression. Full article

(This article belongs to the Special Issue Mobile Sensor Systems and Machine Learning for Gait and Biomechanical Motion Analysis)

► Show Figures

Figure 1

16 pages, 6147 KiB

Open AccessArticle

A Proposal of Implementation of Sitting Posture Monitoring System for Wheelchair Utilizing Machine Learning Methods

by Jawad Ahmad, Johan Sidén and Henrik Andersson

Sensors 2021, 21(19), 6349; https://doi.org/10.3390/s21196349 - 23 Sep 2021

Cited by 19 | Viewed by 4087

Abstract

This paper presents a posture recognition system aimed at detecting sitting postures of a wheelchair user. The main goals of the proposed system are to identify and inform irregular and improper posture to prevent sitting-related health issues such as pressure ulcers, with the potential that it could also be used for individuals without mobility issues. In the proposed monitoring system, an array of 16 screen printed pressure sensor units was employed to obtain pressure data, which are sampled and processed in real-time using read-out electronics. The posture recognition was performed for four sitting positions: right-, left-, forward- and backward leaning based on k-nearest neighbors (k-NN), support vector machines (SVM), random forest (RF), decision tree (DT) and LightGBM machine learning algorithms. As a result, a posture classification accuracy of up to 99.03 percent can be achieved. Experimental studies illustrate that the system can provide real-time pressure distribution value in the form of a pressure map on a standard PC and also on a raspberry pi system equipped with a touchscreen monitor. The stored pressure distribution data can later be shared with healthcare professionals so that abnormalities in sitting patterns can be identified by employing a post-processing unit. The proposed system could be used for risk assessments related to pressure ulcers. It may be served as a benchmark by recording and identifying individuals’ sitting patterns and the possibility of being realized as a lightweight portable health monitoring device. Full article

(This article belongs to the Special Issue Mobile Sensor Systems and Machine Learning for Gait and Biomechanical Motion Analysis)

► Show Figures

Figure 1

13 pages, 1443 KiB

Open AccessArticle

Classical Machine Learning Versus Deep Learning for the Older Adults Free-Living Activity Classification

by Muhammad Awais, Lorenzo Chiari, Espen A. F. Ihlen, Jorunn L. Helbostad and Luca Palmerini

Sensors 2021, 21(14), 4669; https://doi.org/10.3390/s21144669 - 7 Jul 2021

Cited by 15 | Viewed by 3227

Abstract

Physical activity has a strong influence on mental and physical health and is essential in healthy ageing and wellbeing for the ever-growing elderly population. Wearable sensors can provide a reliable and economical measure of activities of daily living (ADLs) by capturing movements through, e.g., accelerometers and gyroscopes. This study explores the potential of using classical machine learning and deep learning approaches to classify the most common ADLs: walking, sitting, standing, and lying. We validate the results on the ADAPT dataset, the most detailed dataset to date of inertial sensor data, synchronised with high frame-rate video labelled data recorded in a free-living environment from older adults living independently. The findings suggest that both approaches can accurately classify ADLs, showing high potential in profiling ADL patterns of the elderly population in free-living conditions. In particular, both long short-term memory (LSTM) networks and Support Vector Machines combined with ReliefF feature selection performed equally well, achieving around 97% F-score in profiling ADLs. Full article

(This article belongs to the Special Issue Mobile Sensor Systems and Machine Learning for Gait and Biomechanical Motion Analysis)

► Show Figures

Figure 1

18 pages, 4623 KiB

Open AccessArticle

Towards Machine Learning-Based Detection of Running-Induced Fatigue in Real-World Scenarios: Evaluation of IMU Sensor Configurations to Reduce Intrusiveness

by Luca Marotta, Jaap H. Buurke, Bert-Jan F. van Beijnum and Jasper Reenalda

Sensors 2021, 21(10), 3451; https://doi.org/10.3390/s21103451 - 15 May 2021

Cited by 18 | Viewed by 4374

Abstract

Physical fatigue is a recurrent problem in running that negatively affects performance and leads to an increased risk of being injured. Identification and management of fatigue helps reducing such negative effects, but is presently commonly based on subjective fatigue measurements. Inertial sensors can record movement data continuously, allowing recording for long durations and extensive amounts of data. Here we aimed to assess if inertial measurement units (IMUs) can be used to distinguish between fatigue levels during an outdoor run with a machine learning classification algorithm trained on IMU-derived biomechanical features, and what is the optimal configuration to do so. Eight runners ran 13 laps of 400 m on an athletic track at a constant speed with 8 IMUs attached to their body (feet, tibias, thighs, pelvis, and sternum). Three segments were extracted from the run: laps 2–4 (no fatigue condition, Rating of Perceived Exertion (RPE) = 6.0 ± 0.0); laps 8–10 (mild fatigue condition, RPE = 11.7 ± 2.0); laps 11–13 (heavy fatigue condition, RPE = 14.2 ± 3.0), run directly after a fatiguing protocol (progressive increase of speed until RPE ≥ 16) that followed lap 10. A random forest classification algorithm was trained with selected features from the 400 m moving average of the IMU-derived accelerations, angular velocities, and joint angles. A leave-one-subject-out cross validation was performed to assess the optimal combination of IMU locations to detect fatigue and selected sensor configurations were considered. The left tibia was the most recurrent sensor location, resulting in accuracies ranging between 0.761 (single left tibia location) and 0.905 (all IMU locations). These findings contribute toward a balanced choice between higher accuracy and lower intrusiveness in the development of IMU-based fatigue detection devices in running. Full article

(This article belongs to the Special Issue Mobile Sensor Systems and Machine Learning for Gait and Biomechanical Motion Analysis)

► Show Figures

Journal Menu

Journal Browser

Mobile Sensor Systems and Machine Learning for Gait and Biomechanical Motion Analysis

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (6 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI