Special Issue "Wearable Gait Sensors"
Deadline for manuscript submissions: closed (15 November 2013)
Prof. Dr. Shigeru Tadano
Division of Human Mechanical Systems and Design, Faculty of Engineering, Hokkaido University, Kita-ku, Kita 13, Nishi 8, Sapporo, 060-8628, Japan
Phone: +81 11 706 6405
Fax: +81 11 706 6405
Interests: bio-mechanical engineering; musculo-skeletal and orthopaedic biomechanics; bone mechanics; medical and healthcare engineering; universal design for aged persons
The availability of multifunctional sensors in combination with small, high capacity power sources in recent years have made it possible for the development of wearable sensor systems. Wearable sensors are small electronic devices placed on the human body to measure various kinds of Data such as acceleration, angular velocity, magnetic fields, EMG, sound etc. Due to their low-cost and convenient manner for measuring data, wearable sensor systems have been attracting attention as a diagnostic or monitoring tool for gait. Wearable gait sensors have the advantage of being able to measure gait for long periods of time and taking measurements outside the clinical office, such as inside the home for health monitoring during daily activities. This will be useful for populations facing an ageing society. Wearable gait sensors may provide information of changes in the body of related to aging, thus making it possible for early diagnosis of patients by clinicians. Though much work has been reported using wearable sensors for gait analysis and health monitoring, the interpretation of the collected data is difficult and this field of research is still a work in progress.
This special issue will address recent technological advancements on wearable sensors intended for gait related applications. We invite review articles and original research papers aimed at proposing new kinds of wearable gait sensor systems, new methods for sensor signal processing, reports on its clinical applications such as health monitoring, rehabilitation and gait analysis.
Prof. Dr. Shigeru Tadano
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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a 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 monthly journal published by MDPI.
- wearable sensor systems
- gait analysis
- diagnostic tool
- health monitoring
- daily activity
- aged motion
Article: Three Dimensional Gait Analysis Using Wearable Acceleration and Gyro Sensors Based on Quaternion Calculations
Sensors 2013, 13(7), 9321-9343; doi:10.3390/s130709321
Received: 3 June 2013; in revised form: 11 July 2013 / Accepted: 17 July 2013 / Published: 19 July 2013| Download PDF Full-text (2422 KB) | Download XML Full-text
Article: Human Body Parts Tracking and Kinematic Features Assessment Based on RSSI and Inertial Sensor Measurements
Sensors 2013, 13(9), 11289-11313; doi:10.3390/s130911289
Received: 3 July 2013; in revised form: 5 August 2013 / Accepted: 10 August 2013 / Published: 23 August 2013| Download PDF Full-text (648 KB) | Download XML Full-text
Article: Measuring Accurate Body Parameters of Dressed Humans with Large-Scale Motion Using a Kinect Sensor
Sensors 2013, 13(9), 11362-11384; doi:10.3390/s130911362
Received: 7 June 2013; in revised form: 20 August 2013 / Accepted: 20 August 2013 / Published: 26 August 2013| Download PDF Full-text (6373 KB) | Download XML Full-text
Article: A Telemetry System Embedded in Clothes for Indoor Localization and Elderly Health Monitoring
Sensors 2013, 13(9), 11728-11749; doi:10.3390/s130911728
Received: 14 July 2013; in revised form: 2 August 2013 / Accepted: 29 August 2013 / Published: 4 September 2013| Download PDF Full-text (1035 KB) | Download XML Full-text
Sensors 2013, 13(10), 13334-13355; doi:10.3390/s131013334
Received: 31 July 2013; in revised form: 10 September 2013 / Accepted: 25 September 2013 / Published: 1 October 2013| Download PDF Full-text (3149 KB) | Download XML Full-text
Sensors 2013, 13(11), 14754-14763; doi:10.3390/s131114754
Received: 17 September 2013; in revised form: 21 October 2013 / Accepted: 28 October 2013 / Published: 30 October 2013| Download PDF Full-text (594 KB)
Sensors 2013, 13(11), 15274-15289; doi:10.3390/s131115274
Received: 21 August 2013; in revised form: 2 November 2013 / Accepted: 4 November 2013 / Published: 8 November 2013| Download PDF Full-text (964 KB)
Sensors 2013, 13(12), 16065-16074; doi:10.3390/s131216065
Received: 3 October 2013; in revised form: 5 November 2013 / Accepted: 18 November 2013 / Published: 26 November 2013| Download PDF Full-text (219 KB)
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Type of Paper: Article
Title: Potential Value of an Activity-Based Feedback System for Treatment of Patients with Chronic Low Back Pain
Authors: MGH Dekker- van Weering 1, MMR Vollenbroek-Hutten 1,2 and HJ Hermens 1,2
Affiliations: 1 Roessingh Research and Development, Telemedicine group, Enschede, The Netherlands; E-Mail: email@example.com
2 University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science, Telemedicine group, Enschede, The Netherlands
Abstract: The aim of this study was to evaluate the potential value of a new personalized activity-based feedback treatment. A prognostic cohort study was performed in patients. The study was carried out in the daily environment of the patients. Seventeen CLBP patients with symptoms more than 3 months from the Netherlands, aged 18-65 years participated. Patients wore a tri-axial accelerometer and a PDA for 15 days. Patients received continuously and time-related personalized feedback and were instructed to follow the activity pattern as displayed on the PDA (norm value). The technical performance and compliance with the system were rated. Objective and subjective activity scores were compared for exploring awareness. The absolute difference between the activity pattern of the patient and the used norm value was calculated and expressed as mean difference. Changes in mean differences and pain intensity levels were tested for exploring the effect of the feedback. The technical performance and compliance with the system were rated moderate. More than half of the patients were aware of their activity level during the feedback days (67%). A positive effect of the feedback was seen in a trend in a decrease in mean differences (p=.149) and a significant decrease in pain intensity levels (p=.005). This study suggests that an individual-tailored feedback system that focuses on the activity behavior of the patient has potential as treatment of patients with CLBP.
Keywords: personalized feedback; daily activities; accelerometry; chronic low back pain
Type of Paper: Article
Title: A Novel Technique to Detect Illegal Race Walking
Authors: James B Lee 1,2, Rebecca B Mellifont 3, Brendan J Burkett 3 and Daniel A James 2,4,5
Affiliations: 1 Charles Darwin University, Darwin, Australia
2 Queensland Sports Technology Cluster, Brisbane, Australia
3 Centre for Healthy Activities Sport and Exercise, University of the Sunshine Coast, Maroochydore, Australia
4 Centre for Wireless Monitoring and Applications, Griffith University, Brisbane, Australia
5 Centre of Excellence for Applied Sport Science Research, Queensland Academy of Sport, Brisbane, Australia
Abstract: Judging of race walking for Olympic or World Championships relies on subjective human observation to detect illegal gait. The aim was to determine whether a single, inertial sensor could improve this measure, ensuring that any illegal steps are detected. Seven competitive race walkers performed a series of legal, illegal and self selected pace race walks. The athletes wore a single inertial sensor placed on S1 of the sacrum and data were collected simultaneously with a high speed camera. When comparing the data, the typical error of estimate was 0.02 s (95% CL 0.01–0.02), with a bias of 0.02 (±0.01). An inertial sensor was found to improve accuracy of detecting loss of ground contact, therefore be a valuable tool to assist judges during race walk events.
Type of Paper: Article
Title: At Home Assessment of Turning Mobility in Parkinson’s Disease and Elderly People
Authors: Martina Mancini 1, Mahmoud El-Gohary 2, James McNames 1,2,3 and Fay Horak 1,2
Affiliations: 1 Oregon Health & Science University, USA
2 APDM, Inc., USA
3 Portland State University, Portland, OR 97207-0751, USA; E-Mail: firstname.lastname@example.org
Abstract: Difficulty with turning is a major contributor to mobility disability, falls, and reduced quality of life in older people and people with movement disorders, such as Parkinson’s disease (PD). Continuous monitoring of turning with wearable sensors during normal, spontaneous daily activities may help clinicians, patients, and caregivers determine who is at risk of falls and could benefits from preventative interventions such as assistive aids, exercise, drug changes, and physical therapy. We show that continuous monitoring of natural turning with wearable sensors during daily activities inside and outside the home is feasible for people with PD and elderly people.
Type of Paper: Article
Title: Comparison of Raw Acceleration from the GENEA and ActiGraph™ GT3X+ activity monitors
Authors: Dinesh John 1, Ph.D., Jeffer Sasaki 2, Ph.D., John Staudenmayer 2, Ph.D., Mariana Mavilia 2, B.S. and Patty S. Freedson 2, Ph.D., FACSM
Affiliations: 1 Northeastern University, Boston, MA- 02115, USA; E-Mail: email@example.com
2 University of Massachusetts, Amherst, MA- 01003, USA
Abstract: We compared mean triaxial vector magnitudes (VM) from the commonly used ActiGraph™ GT3X+ and GENEA activity monitors during shaker testing. Inter-monitor activity type recognition accuracies were compared for two models (a) frequency-domain (FD) and (b) time-domain (TD) model to predict lab-based treadmill and free-living activities. We compared prediction accuracies when the model was fit using one monitor and then applied to the other. During shaker testing, GENEA produced significantly higher (p<0.05) mean VM than GT3X+. Training the model using TD input features on the GENEA and applying to GT3X+ data lowered prediction accuracy (p<0.05). It may be inappropriate to interchange models among monitors when input features are TD attributes of raw acceleration.
Type of Paper: Article
Title: Physical Human Activity Recognition Using Wearable Sensors
Authors: Mariam Dedabrishvili 1, Samer Mohammed 1, Faicel Chamroukhi 2, Latifa Oukhellou 3 and Yacine Amirat 1
Affiliations: 1 University Paris-Est Créteil (UPEC), LISSI, 122 rue Paul Armangot, 94400 Vitry-Sur-Seine, France; E-Mail: firstname.lastname@example.org
2 Université de Toulon, CNRS, LSIS, UMR7296, Bâtiment R, BP 20132, 83957 La Garde Cedex, France
3 University Paris-Est, IFSTTAR, GRETTIA, F-93166 Noisy-le-Grand, France
Abstract: The present paper deals with the human daily living activities recognition using wearable inertial sensors. Extensive and systematic comparison between various classification techniques is done to recognize activities performed by individuals. Activity recognition is done using different sensor units worn by the wearer at key points of upper/lower body limbs. The feature extraction procedure is performed prior to the classification process. Supervised machine learning techniques such as k-nearest neighbor (kNN), support vector machines (SVM), Naive Bayes classifier, etc. as well as unsupervised learning classification techniques such as k-means, mixture models, hierarchical clustering, hidden Markov model (HMM), etc., are used. The paper is oriented on the issue of developing new smart spaces and notices the best way of using wearable sensors in terms of activity detection. Moreover, sensor properties and sensor placements on the human body are reviewed. The effectiveness of existing approaches starting from data acquisition, ending with classification and recognition rates, is conducted through real time experiments with different subjects.
Keywords: activity recognition; wearable sensors; smart spaces; data classifiers; accelerometers; physical activities
Last update: 19 August 2013