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Inertial Sensors for Gait Recognition and Analysis

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

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 15361

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Special Issue Editors

Prof. Dr. Joan Cabestany

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Guest Editor

ISSET Research Group (Integrated Smart Sensors and Health Technologies), Department of Electronic Engineering, Universitat Politcnica de Catalunya, 08034 Barcelona, Spain
Interests: movement diseases; people with gait problems; the application of electronic and communication engineering in Parkinson Disease; identification and measurement of Parkinson Disease-related symptoms and falls
Special Issues, Collections and Topics in MDPI journals

Dr. Carlos Pérez-López

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Guest Editor

Consorci Sanitari de l'Alt Penedès i Garraf (CSAPG), 08810 Sant Pere de Ribes, Spain
Interests: wearables; embedded algorithms; e-health; parkinson disease; related pathologies with ageing: frailty

Special Issue Information

Dear Colleagues,

Inertial sensors have been used over the years for multiple purposes (among them, gait and movement analysis). Due to the great development of MEMs technology, the use of these sensors has become widespread and they are included in a great number of devices used in our daily activity. The main advantages of these inertial sensors are their related characteristics: they are very small with a very low consumption rate and, therefore, can be installed almost anywhere.

In this way, in recent years, the amount of data available from devices based on inertial systems has grown exponentially, leading to the possibility of new developments and services based on specific aspects of human mobility. Wearable inertial systems are part of this growth and can provide relevant information in different scenarios and applications (health, wellbeing, sport, home activity, etc.).

Specifically, gait analysis is a topic that, due to the complexity of the movement itself, offers to clinicians valuable information for the study of multiple pathologies. Continuous gait analysis will allow the continuous monitoring of highly relevant aspects of people's health status or even continuous monitoring of rehabilitation processes in many fields.

In this framework, we are very pleased to edit this Special Issue on "Inertial Sensors for Gait Recognition and Analysis". The Special Issue will deal with analysis procedures applied to inertial sensors’ data, covering from improvements in the devices’ hardware to the embedded algorithmic set.

Prof. Dr. Joan Cabestany
Dr. Carlos Pérez-López
Guest Editors

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Keywords

Inertial Sensors
Wearables
Gait Analysis
Sensor fusion
Algorithms
Continuous monitoring
Diagnosis assessment
Person-centered new health services

Published Papers (5 papers)

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16 pages, 7914 KiB

Open AccessArticle

Towards a Mobile Gait Analysis for Patients with a Spinal Cord Injury: A Robust Algorithm Validated for Slow Walking Speeds

by Charlotte Werner, Chris Awai Easthope, Armin Curt and László Demkó

Sensors 2021, 21(21), 7381; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217381 - 06 Nov 2021

Cited by 11 | Viewed by 2459

Abstract

Spinal cord injury (SCI) patients suffer from diverse gait deficits depending on the severity of their injury. Gait assessments can objectively track the progress during rehabilitation and support clinical decision making, but a comprehensive gait analysis requires far more complex setups and time-consuming protocols that are not feasible in the daily clinical routine. As using inertial sensors for mobile gait analysis has started to gain ground, this work aimed to develop a sensor-based gait analysis for the specific population of SCI patients that measures the spatio-temporal parameters of typical gait laboratories for day-to-day clinical applications. The proposed algorithm uses shank-mounted inertial sensors and personalized thresholds to detect steps and gait events according to the individual gait profiles. The method was validated in nine SCI patients and 17 healthy controls walking on an instrumented treadmill while wearing reflective markers for motion capture used as a gold standard. The sensor-based algorithm (i) performed similarly well for the two cohorts and (ii) is robust enough to cover the diverse gait deficits of SCI patients, from slow (0.3 m/s) to preferred walking speeds. Full article

(This article belongs to the Special Issue Inertial Sensors for Gait Recognition and Analysis)

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17 pages, 2422 KiB

Open AccessArticle

Wearable Sensor-Based Prediction Model of Timed up and Go Test in Older Adults

by Jungyeon Choi, Sheridan M. Parker, Brian A. Knarr, Yeongjin Gwon and Jong-Hoon Youn

Sensors 2021, 21(20), 6831; https://0-doi-org.brum.beds.ac.uk/10.3390/s21206831 - 14 Oct 2021

Cited by 12 | Viewed by 2227

Abstract

The Timed Up and Go (TUG) test has been frequently used to assess the risk of falls in older adults because it is an easy, fast, and simple method of examining functional mobility and balance without special equipment. The purpose of this study is to develop a model that predicts the TUG test using three-dimensional acceleration data collected from wearable sensors during normal walking. We recruited 37 older adults for an outdoor walking task, and seven inertial measurement unit (IMU)-based sensors were attached to each participant. The elastic net and ridge regression methods were used to reduce gait feature sets and build a predictive model. The proposed predictive model reliably estimated the participants’ TUG scores with a small margin of prediction errors. Although the prediction accuracies with two foot-sensors were slightly better than those of other configurations (e.g., MAPE: foot (0.865 s) > foot and pelvis (0.918 s) > pelvis (0.921 s)), we recommend the use of a single IMU sensor at the pelvis since it would provide wearing comfort while avoiding the disturbance of daily activities. The proposed predictive model can enable clinicians to assess older adults’ fall risks remotely through the evaluation of the TUG score during their daily walking. Full article

(This article belongs to the Special Issue Inertial Sensors for Gait Recognition and Analysis)

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12 pages, 1969 KiB

Open AccessArticle

Design and Preliminary Validation of Individual Customized Insole for Adults with Flexible Flatfeet Based on the Plantar Pressure Redistribution

by Yangzheng Jiang, Duojin Wang, Jiming Ying, Pengfei Chu, Yu Qian and Wenming Chen

Sensors 2021, 21(5), 1780; https://0-doi-org.brum.beds.ac.uk/10.3390/s21051780 - 04 Mar 2021

Cited by 15 | Viewed by 3560

Abstract

Flatfoot is a common musculoskeletal deformity. One of the most effective treatments is to wear individually customized plantar pressure-based insoles to help users change the abnormally distributed pressure on the pelma. However, most previous studies were divided only into several plantar areas without detailed plantar characteristic analysis. In this study, a new insole is designed which redistributes pressure following the analysis of characteristic points of plantar pressure, and practical evaluation during walking of subjects while wearing the insole. In total, 10 subjects with flexible flatfeet have participated in the performance of gait experiments by wearing flat insoles, orthotic insoles, and plantar pressure redistribution insoles (PPRI). The results showed that the stance time of PPRI was significantly lower than that of the flat insoles under slow gait. PPRI in the second to third metatarsal and medial heel area showed better unloading capabilities than orthotic insoles. In the metatarsal and heel area, the PPRI also had its advantage in percentage of contact area compared to flat insole and orthotic insole. The results prove that PPRI improves the plantar pressure distribution and gait efficiency of adults with flexible flatfeet, and can be applied into clinical application. Full article

(This article belongs to the Special Issue Inertial Sensors for Gait Recognition and Analysis)

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13 pages, 1306 KiB

Open AccessArticle

Sensor-Based and Patient-Based Assessment of Daily-Living Physical Activity in People with Parkinson’s Disease: Do Motor Subtypes Play a Role?

by Irina Galperin, Talia Herman, Mira Assad, Natalie Ganz, Anat Mirelman, Nir Giladi and Jeffrey M. Hausdorff

Sensors 2020, 20(24), 7015; https://0-doi-org.brum.beds.ac.uk/10.3390/s20247015 - 08 Dec 2020

Cited by 9 | Viewed by 2436

Abstract

The benefits of daily-living physical activity are clear. Nonetheless, the relationship between physical activity levels and motor subtypes of Parkinson’s disease (PD), i.e., tremor dominant (TD) and postural instability gait difficulty (PIGD), have not been well-studied. It is also unclear if patient perspectives and motor symptom severity are related to objective, sensor-based assessment of daily-living activity in those subtypes. To address these questions, total daily-living physical activity was quantified in 73 patients with PD and 29 healthy controls using a 3D-accelerometer worn on the lower back for at least three days. We found that individuals with the PIGD subtype were significantly less active than healthy older adults (p = 0.007), unlike individuals with the TD subtype. Among the PIGD subtype, higher daily physical activity was negatively associated with more severe ON bradykinesia (r_S = -0.499, p = 0.002), motor symptoms (higher ON MDS-UPDRS (Unified Parkinson’s Disease Rating Scale motor examination)-III scores), gait difficulties (r_S = -0.502, p = 0.002), motor complications (r_S = 0.466, p = 0.004), and balance (r_S = 0.519, p = 0.001). In contrast, among the TD subtype, disease-related characteristics were not related to daily-living physical activity. Intriguingly, physical activity was not related to self-report of ADL difficulties (scores of the MDS-UPDRS Parts I or II) in both motor subtypes. These findings highlight the importance of objective daily-living physical activity monitoring and suggest that self-report does not necessarily reflect objective physical activity levels. Furthermore, the results point to important differences in factors related to physical activity in PD motor subtypes, setting the stage for personalized treatment programs. Full article

(This article belongs to the Special Issue Inertial Sensors for Gait Recognition and Analysis)

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12 pages, 3900 KiB

Open AccessLetter

Gait Event Detection for Stroke Patients during Robot-Assisted Gait Training

by Andreas Schicketmueller, Juliane Lamprecht, Marc Hofmann, Michael Sailer and Georg Rose

Sensors 2020, 20(12), 3399; https://0-doi-org.brum.beds.ac.uk/10.3390/s20123399 - 16 Jun 2020

Cited by 11 | Viewed by 3893

Abstract

Functional electrical stimulation and robot-assisted gait training are techniques which are used in a clinical routine to enhance the rehabilitation process of stroke patients. By combining these technologies, therapy effects could be further improved and the rehabilitation process can be supported. In order to combine these technologies, a novel algorithm was developed, which aims to extract gait events based on movement data recorded with inertial measurement units. In perspective, the extracted gait events can be used to trigger functional electrical stimulation during robot-assisted gait training. This approach offers the possibility of equipping a broad range of potential robot-assisted gait trainers with functional electrical stimulation. In particular, the aim of this study was to test the robustness of the previously developed algorithm in a clinical setting with patients who suffered a stroke. A total amount of N = 10 stroke patients participated in the study, with written consent. The patients were assigned to two different robot-assisted gait trainers (Lyra and Lokomat) according to their performance level, resulting in five recording sessions for each gait-trainer. A previously developed algorithm was applied and further optimized in order to extract the gait events. A mean detection rate across all patients of 95.8% ± 7.5% for the Lyra and 98.7% ± 2.6% for the Lokomat was achieved. The mean type 1 error across all patients was 1.0% ± 2.0% for the Lyra and 0.9% ± 2.3% for the Lokomat. As a result, the developed algorithm was robust against patient specific movements, and provided promising results for the further development of a technique that can detect gait events during robot-assisted gait training, with the future aim to trigger functional electrical stimulation. Full article

(This article belongs to the Special Issue Inertial Sensors for Gait Recognition and Analysis)

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