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Wearables for Movement Analysis in Healthcare

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 52167

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Special Issue Editors

Ospedale San Giuseppe, Istituto Auxologico Italiano, IRCCS, Strada Luigi Cadorna 90, 28824 Piancavallo, VB, Italy
Interests: IMU; physical and rehabilitation medicine; functional evaluation and instrumental assessment; ageing and pathological conditions; spinal cord injuries; musculoskeletal disorders; obesity and metabolic conditions; monitoring physical work load in health workers and other occupational activities
Special Issues, Collections and Topics in MDPI journals
Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
Interests: bioengineering; movement analysis; biomechanics; rehabilitation; healthcare
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in technology offer one solution to innovate healthcare and meet demand at a low cost. Wearable sensors are the most promising technology when it comes to: (a) supporting health and social care providers in delivering safe, more efficient, and cost-effective care, (b) improving people’s ability to self-manage their health and well-being, (c) alerting healthcare professionals about changes in their condition, and (d) supporting adherence to the prescribed intervention. A variety of compact wearable sensors available today have allowed researchers and clinicians to pursue applications in which individuals are monitored at home and in community settings. Wearable sensors can help to reduce time devoted to assessment and provide objective, quantifiable data on patients’ capabilities, unobtrusively and continuously. These technologies provide the opportunity not only to study motor function while patients perform daily-life activities but also to provide timely, meaningful feedback to patients and their physiotherapists.

In this Special Issue, we invite original research papers and review articles aimed at promoting novel wearable technology for movement analysis, methods for sensor signal processing, as well as on field experiences of their applications in healthcare.

Prof. Dr. Paolo Capodaglio
Prof. Dr. Veronica Cimolin
Guest Editors

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Keywords

  • wearable technology
  • wearable sensors
  • movement analysis
  • healthcare
  • rehabilitation

Published Papers (16 papers)

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Editorial

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2 pages, 180 KiB  
Editorial
Wearables for Movement Analysis in Healthcare
by Paolo Capodaglio and Veronica Cimolin
Sensors 2022, 22(10), 3720; https://0-doi-org.brum.beds.ac.uk/10.3390/s22103720 - 13 May 2022
Cited by 1 | Viewed by 1185
Abstract
Quantitative movement analysis is widely used in clinical practice and research to objectively and thoroughly investigate movement disorder [...] Full article
(This article belongs to the Special Issue Wearables for Movement Analysis in Healthcare)

Research

Jump to: Editorial, Other

24 pages, 2132 KiB  
Article
Computation of Gait Parameters in Post Stroke and Parkinson’s Disease: A Comparative Study Using RGB-D Sensors and Optoelectronic Systems
by Veronica Cimolin, Luca Vismara, Claudia Ferraris, Gianluca Amprimo, Giuseppe Pettiti, Roberto Lopez, Manuela Galli, Riccardo Cremascoli, Serena Sinagra, Alessandro Mauro and Lorenzo Priano
Sensors 2022, 22(3), 824; https://0-doi-org.brum.beds.ac.uk/10.3390/s22030824 - 21 Jan 2022
Cited by 22 | Viewed by 3182
Abstract
The accurate and reliable assessment of gait parameters is assuming an important role, especially in the perspective of designing new therapeutic and rehabilitation strategies for the remote follow-up of people affected by disabling neurological diseases, including Parkinson’s disease and post-stroke injuries, in particular [...] Read more.
The accurate and reliable assessment of gait parameters is assuming an important role, especially in the perspective of designing new therapeutic and rehabilitation strategies for the remote follow-up of people affected by disabling neurological diseases, including Parkinson’s disease and post-stroke injuries, in particular considering how gait represents a fundamental motor activity for the autonomy, domestic or otherwise, and the health of neurological patients. To this end, the study presents an easy-to-use and non-invasive solution, based on a single RGB-D sensor, to estimate specific features of gait patterns on a reduced walking path compatible with the available spaces in domestic settings. Traditional spatio-temporal parameters and features linked to dynamic instability during walking are estimated on a cohort of ten parkinsonian and eleven post-stroke subjects using a custom-written software that works on the result of a body-tracking algorithm. Then, they are compared with the “gold standard” 3D instrumented gait analysis system. The statistical analysis confirms no statistical difference between the two systems. Data also indicate that the RGB-D system is able to estimate features of gait patterns in pathological individuals and differences between them in line with other studies. Although they are preliminary, the results suggest that this solution could be clinically helpful in evolutionary disease monitoring, especially in domestic and unsupervised environments where traditional gait analysis is not usable. Full article
(This article belongs to the Special Issue Wearables for Movement Analysis in Healthcare)
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10 pages, 1610 KiB  
Article
A Comparative Analysis of Shoes Designed for Subjects with Obesity Using a Single Inertial Sensor: Preliminary Results
by Veronica Cimolin, Michele Gobbi, Camillo Buratto, Samuele Ferraro, Andrea Fumagalli, Manuela Galli and Paolo Capodaglio
Sensors 2022, 22(3), 782; https://0-doi-org.brum.beds.ac.uk/10.3390/s22030782 - 20 Jan 2022
Cited by 7 | Viewed by 1859
Abstract
Walking remains a highly recommended form of exercise for the management of obesity. Thus, comfortable and adequate shoes represent, together with the prescription of a safe adapted physical activity, an important means to achieve the recommended physical activity target volume. However, the literature [...] Read more.
Walking remains a highly recommended form of exercise for the management of obesity. Thus, comfortable and adequate shoes represent, together with the prescription of a safe adapted physical activity, an important means to achieve the recommended physical activity target volume. However, the literature on shoes specific for obese individuals is inadequate. The aim of the present study was to compare the performance of shoes specifically designed for subjects with obesity with everyday sneakers during instrumented 6-min walking test and outdoor 30-min ambulation in a group of subjects with obesity using a single wearable device. Twenty-three obese individuals (mean age 58.96 years) were recruited and classified into two groups: deconditioned (n = 13) and non-deconditioned patients (n = 10). Each participant was evaluated with his/her daily sneakers and the day after with shoes specifically designed for people with obesity by means of a questionnaire related to the comfort related to each model of shoes and instrumentally during the i6MWT and an outdoor walking test. The results showed that the specifically designed shoes displayed the higher score as for comfort, in particular in the deconditioned group. During the i6MWT, the distance walked, and step length significantly increased in the deconditioned group when specifically designed shoes were worn; no significant changes were observed in the non-deconditioned individuals. The deconditioned group displayed longer step length during the outdoor 30-min ambulation test. In the non-deconditioned group, the use of specific shoes correlated to better performance in terms of gait speed and cadence. These data, although preliminary, seem to support the hypothesis that shoes specifically conceived and designed for counteracting some of the known functional limitations in subjects with obesity allow for a smoother, more stable and possibly less fatiguing gait schema over time. Full article
(This article belongs to the Special Issue Wearables for Movement Analysis in Healthcare)
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20 pages, 2837 KiB  
Article
Use of a Single Wearable Sensor to Evaluate the Effects of Gait and Pelvis Asymmetries on the Components of the Timed Up and Go Test, in Persons with Unilateral Lower Limb Amputation
by Maria Stella Valle, Antonino Casabona, Ilenia Sapienza, Luca Laudani, Alessandro Vagnini, Sara Lanza and Matteo Cioni
Sensors 2022, 22(1), 95; https://0-doi-org.brum.beds.ac.uk/10.3390/s22010095 - 24 Dec 2021
Cited by 4 | Viewed by 2566
Abstract
The Timed Up and Go (TUG) test quantifies physical mobility by measuring the total performance time. In this study, we quantified the single TUG subcomponents and, for the first time, explored the effects of gait cycle and pelvis asymmetries on them. Transfemoral (TF) [...] Read more.
The Timed Up and Go (TUG) test quantifies physical mobility by measuring the total performance time. In this study, we quantified the single TUG subcomponents and, for the first time, explored the effects of gait cycle and pelvis asymmetries on them. Transfemoral (TF) and transtibial (TT) amputees were compared with a control group. A single wearable inertial sensor, applied to the back, captured kinematic data from the body and pelvis during the 10-m walk test and the TUG test. From these data, two categories of symmetry indexes (SI) were computed: One SI captured the differences between the antero-posterior accelerations of the two sides during the gait cycle, while another set of SI quantified the symmetry over the three-dimensional pelvis motions. Moreover, the total time of the TUG test, the time of each subcomponent, and the velocity of the turning subcomponents were measured. Only the TF amputees showed significant reductions in each SI category when compared to the controls. During the TUG test, the TF group showed a longer duration and velocity reduction mainly over the turning subtasks. However, for all the amputees there were significant correlations between the level of asymmetries and the velocity during the turning tasks. Overall, gait cycle and pelvis asymmetries had a specific detrimental effect on the turning performance instead of on linear walking. Full article
(This article belongs to the Special Issue Wearables for Movement Analysis in Healthcare)
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16 pages, 1556 KiB  
Article
Rapid Dynamic Naturalistic Monitoring of Bradykinesia in Parkinson’s Disease Using a Wrist-Worn Accelerometer
by Jeroen G. V. Habets, Christian Herff, Pieter L. Kubben, Mark L. Kuijf, Yasin Temel, Luc J. W. Evers, Bastiaan R. Bloem, Philip A. Starr, Ro’ee Gilron and Simon Little
Sensors 2021, 21(23), 7876; https://0-doi-org.brum.beds.ac.uk/10.3390/s21237876 - 26 Nov 2021
Cited by 14 | Viewed by 2619
Abstract
Motor fluctuations in Parkinson’s disease are characterized by unpredictability in the timing and duration of dopaminergic therapeutic benefits on symptoms, including bradykinesia and rigidity. These fluctuations significantly impair the quality of life of many Parkinson’s patients. However, current clinical evaluation tools are not [...] Read more.
Motor fluctuations in Parkinson’s disease are characterized by unpredictability in the timing and duration of dopaminergic therapeutic benefits on symptoms, including bradykinesia and rigidity. These fluctuations significantly impair the quality of life of many Parkinson’s patients. However, current clinical evaluation tools are not designed for the continuous, naturalistic (real-world) symptom monitoring needed to optimize clinical therapy to treat fluctuations. Although commercially available wearable motor monitoring, used over multiple days, can augment neurological decision making, the feasibility of rapid and dynamic detection of motor fluctuations is unclear. So far, applied wearable monitoring algorithms are trained on group data. In this study, we investigated the influence of individual model training on short timescale classification of naturalistic bradykinesia fluctuations in Parkinson’s patients using a single-wrist accelerometer. As part of the Parkinson@Home study protocol, 20 Parkinson patients were recorded with bilateral wrist accelerometers for a one hour OFF medication session and a one hour ON medication session during unconstrained activities in their own homes. Kinematic metrics were extracted from the accelerometer data from the bodyside with the largest unilateral bradykinesia fluctuations across medication states. The kinematic accelerometer features were compared over the 1 h duration of recording, and medication-state classification analyses were performed on 1 min segments of data. Then, we analyzed the influence of individual versus group model training, data window length, and total number of training patients included in group model training, on classification. Statistically significant areas under the curves (AUCs) for medication induced bradykinesia fluctuation classification were seen in 85% of the Parkinson patients at the single minute timescale using the group models. Individually trained models performed at the same level as the group trained models (mean AUC both 0.70, standard deviation respectively 0.18 and 0.10) despite the small individual training dataset. AUCs of the group models improved as the length of the feature windows was increased to 300 s, and with additional training patient datasets. We were able to show that medication-induced fluctuations in bradykinesia can be classified using wrist-worn accelerometry at the time scale of a single minute. Rapid, naturalistic Parkinson motor monitoring has the clinical potential to evaluate dynamic symptomatic and therapeutic fluctuations and help tailor treatments on a fast timescale. Full article
(This article belongs to the Special Issue Wearables for Movement Analysis in Healthcare)
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13 pages, 897 KiB  
Article
Kinematics Adaptation and Inter-Limb Symmetry during Gait in Obese Adults
by Massimiliano Pau, Paolo Capodaglio, Bruno Leban, Micaela Porta, Manuela Galli and Veronica Cimolin
Sensors 2021, 21(17), 5980; https://0-doi-org.brum.beds.ac.uk/10.3390/s21175980 - 06 Sep 2021
Cited by 13 | Viewed by 2986
Abstract
The main purpose of this study is to characterize lower limb joint kinematics during gait in obese individuals by analyzing inter-limb symmetry and angular trends of lower limb joints during walking. To this purpose, 26 obese individuals (mean age 28.5 years) and 26 [...] Read more.
The main purpose of this study is to characterize lower limb joint kinematics during gait in obese individuals by analyzing inter-limb symmetry and angular trends of lower limb joints during walking. To this purpose, 26 obese individuals (mean age 28.5 years) and 26 normal-weight age- and sex-matched were tested using 3D gait analysis. Raw kinematic data were processed to derive joint-specific angle trends and angle-angle diagrams (synchronized cyclograms) which were characterized in terms of area, orientation and trend symmetry parameters. The results show that obese individuals exhibit a kinematic pattern which significantly differs from those of normal weight especially in the stance phase. In terms of inter-limb symmetry, higher values were found in obese individuals for all the considered parameters, even though the statistical significance was detected only in the case of trend symmetry index at ankle joint. The described alterations of gait kinematics in the obese individuals and especially the results on gait asymmetry are important, because the cyclic uneven movement repeated for hours daily can involve asymmetrical spine loading and cause lumbar pain and could be dangerous for overweight individuals. Full article
(This article belongs to the Special Issue Wearables for Movement Analysis in Healthcare)
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9 pages, 1181 KiB  
Article
Measurement of Ankle Joint Movements Using IMUs during Running
by Byong Hun Kim, Sung Hyun Hong, In Wook Oh, Yang Woo Lee, In Ho Kee and Sae Yong Lee
Sensors 2021, 21(12), 4240; https://0-doi-org.brum.beds.ac.uk/10.3390/s21124240 - 21 Jun 2021
Cited by 16 | Viewed by 4137
Abstract
Gait analysis has historically been implemented in laboratory settings only with expensive instruments; yet, recently, efforts to develop and integrate wearable sensors into clinical applications have been made. A limited number of previous studies have been conducted to validate inertial measurement units (IMUs) [...] Read more.
Gait analysis has historically been implemented in laboratory settings only with expensive instruments; yet, recently, efforts to develop and integrate wearable sensors into clinical applications have been made. A limited number of previous studies have been conducted to validate inertial measurement units (IMUs) for measuring ankle joint kinematics, especially with small movement ranges. Therefore, the purpose of this study was to validate the ability of available IMUs to accurately measure the ankle joint angles by comparing the ankle joint angles measured using a wearable device with those obtained using a motion capture system during running. Ten healthy subjects participated in the study. The intraclass correlation coefficient (ICC) and standard error of measurement were calculated for reliability, whereas the Pearson coefficient correlation was performed for validity. The results showed that the day-to-day reliability was excellent (0.974 and 0.900 for sagittal and frontal plane, respectively), and the validity was good in both sagittal (r = 0.821, p < 0.001) and frontal (r = 0.835, p < 0.001) planes for ankle joints. In conclusion, we suggest that the developed device could be used as an alternative tool for the 3D motion capture system for assessing ankle joint kinematics. Full article
(This article belongs to the Special Issue Wearables for Movement Analysis in Healthcare)
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11 pages, 1817 KiB  
Article
Sensor Network for Analyzing Upper Body Strategies in Parkinson’s Disease versus Normative Kinematic Patterns
by Paola Romano, Sanaz Pournajaf, Marco Ottaviani, Annalisa Gison, Francesco Infarinato, Claudia Mantoni, Maria Francesca De Pandis, Marco Franceschini and Michela Goffredo
Sensors 2021, 21(11), 3823; https://0-doi-org.brum.beds.ac.uk/10.3390/s21113823 - 31 May 2021
Cited by 8 | Viewed by 2193
Abstract
In rehabilitation, the upper limb function is generally assessed using clinical scales and functional motor tests. Although the Box and Block Test (BBT) is commonly used for its simplicity and ease of execution, it does not provide a quantitative measure of movement quality. [...] Read more.
In rehabilitation, the upper limb function is generally assessed using clinical scales and functional motor tests. Although the Box and Block Test (BBT) is commonly used for its simplicity and ease of execution, it does not provide a quantitative measure of movement quality. This study proposes the integration of an ecological Inertial Measurement Units (IMUs) system for analysis of the upper body kinematics during the execution of a targeted version of BBT, by able-bodied persons with subjects with Parkinson’s disease (PD). Joint angle parameters (mean angle and range of execution) and hand trajectory kinematic indices (mean velocity, mean acceleration, and dimensionless jerk) were calculated from the data acquired by a network of seven IMUs. The sensors were applied on the trunk, head, and upper limb in order to characterize the motor strategy used during the execution of BBT. Statistics revealed significant differences (p < 0.05) between the two groups, showing compensatory strategies in subjects with PD. The proposed IMU-based targeted BBT protocol allows to assess the upper limb function during manual dexterity tasks and could be used in the future for assessing the efficacy of rehabilitative treatments. Full article
(This article belongs to the Special Issue Wearables for Movement Analysis in Healthcare)
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14 pages, 4176 KiB  
Communication
Rehabilitation and Return to Sport Assessment after Anterior Cruciate Ligament Injury: Quantifying Joint Kinematics during Complex High-Speed Tasks through Wearable Sensors
by Stefano Di Paolo, Nicola Francesco Lopomo, Francesco Della Villa, Gabriele Paolini, Giulio Figari, Laura Bragonzoni, Alberto Grassi and Stefano Zaffagnini
Sensors 2021, 21(7), 2331; https://0-doi-org.brum.beds.ac.uk/10.3390/s21072331 - 26 Mar 2021
Cited by 33 | Viewed by 5239
Abstract
The aim of the present study was to quantify joint kinematics through a wearable sensor system in multidirectional high-speed complex movements used in a protocol for rehabilitation and return to sport assessment after Anterior Cruciate Ligament (ACL) injury, and to validate it against [...] Read more.
The aim of the present study was to quantify joint kinematics through a wearable sensor system in multidirectional high-speed complex movements used in a protocol for rehabilitation and return to sport assessment after Anterior Cruciate Ligament (ACL) injury, and to validate it against a gold standard optoelectronic marker-based system. Thirty-four healthy athletes were evaluated through a full-body wearable sensor (MTw Awinda, Xsens) and a marker-based optoelectronic (Vicon Nexus, Vicon) system during the execution of three tasks: drop jump, forward sprint, and 90° change of direction. Clinically relevant joint angles of lower limbs and trunk were compared through Pearson’s correlation coefficient (r), and the Coefficient of Multiple Correlation (CMC). An excellent agreement (r > 0.94, CMC > 0.96) was found for knee and hip sagittal plane kinematics in all the movements. A fair-to-excellent agreement was found for frontal (r 0.55–0.96, CMC 0.63–0.96) and transverse (r 0.45–0.84, CMC 0.59–0.90) plane kinematics. Movement complexity slightly affected the agreement between the systems. The system based on wearable sensors showed fair-to-excellent concurrent validity in the evaluation of the specific joint parameters commonly used in rehabilitation and return to sport assessment after ACL injury for complex movements. The ACL professionals could benefit from full-body wearable technology in the on-field rehabilitation of athletes. Full article
(This article belongs to the Special Issue Wearables for Movement Analysis in Healthcare)
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11 pages, 500 KiB  
Communication
Functional Electrical Stimulation for Foot Drop in Post-Stroke People: Quantitative Effects on Step-to-Step Symmetry of Gait Using a Wearable Inertial Sensor
by Giulia Schifino, Veronica Cimolin, Massimiliano Pau, Maira Jaqueline da Cunha, Bruno Leban, Micaela Porta, Manuela Galli and Aline Souza Pagnussat
Sensors 2021, 21(3), 921; https://0-doi-org.brum.beds.ac.uk/10.3390/s21030921 - 29 Jan 2021
Cited by 12 | Viewed by 3986
Abstract
The main purpose of the present study was to assess the effects of foot drop stimulators (FDS) in individuals with stroke by means of spatio-temporal and step-to-step symmetry, harmonic ratio (HR), parameters obtained from trunk accelerations acquired using a wearable inertial sensor. Thirty-two [...] Read more.
The main purpose of the present study was to assess the effects of foot drop stimulators (FDS) in individuals with stroke by means of spatio-temporal and step-to-step symmetry, harmonic ratio (HR), parameters obtained from trunk accelerations acquired using a wearable inertial sensor. Thirty-two patients (age: 56.84 ± 9.10 years; 68.8% male) underwent an instrumental gait analysis, performed using a wearable inertial sensor before and a day after the 10-session treatment (PRE and POST sessions). The treatment consisted of 10 sessions of 20 min of walking on a treadmill while using the FDS device. The spatio-temporal parameters and the HR in the anteroposterior (AP), vertical (V), and mediolateral (ML) directions were computed from trunk acceleration data. The results showed that time had a significant effect on the spatio-temporal parameters; in particular, a significant increase in gait speed was detected. Regarding the HRs, the HR in the ML direction was found to have significantly increased (+20%), while those in the AP and V directions decreased (approximately 13%). Even if further studies are necessary, from these results, the HR seems to provide additional information on gait patterns with respect to the traditional spatio-temporal parameters, advancing the assessment of the effects of FDS devices in stroke patients. Full article
(This article belongs to the Special Issue Wearables for Movement Analysis in Healthcare)
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13 pages, 3143 KiB  
Article
Machine-Learning Based Determination of Gait Events from Foot-Mounted Inertial Units
by Matteo Zago, Marco Tarabini, Martina Delfino Spiga, Cristina Ferrario, Filippo Bertozzi, Chiarella Sforza and Manuela Galli
Sensors 2021, 21(3), 839; https://0-doi-org.brum.beds.ac.uk/10.3390/s21030839 - 27 Jan 2021
Cited by 13 | Viewed by 2580
Abstract
A promising but still scarcely explored strategy for the estimation of gait parameters based on inertial sensors involves the adoption of machine learning techniques. However, existing approaches are reliable only for specific conditions, inertial measurements unit (IMU) placement on the body, protocols, or [...] Read more.
A promising but still scarcely explored strategy for the estimation of gait parameters based on inertial sensors involves the adoption of machine learning techniques. However, existing approaches are reliable only for specific conditions, inertial measurements unit (IMU) placement on the body, protocols, or when combined with additional devices. In this paper, we tested an alternative gait-events estimation approach which is fully data-driven and does not rely on a priori models or assumptions. High-frequency (512 Hz) data from a commercial inertial unit were recorded during 500 steps performed by 40 healthy participants. Sensors’ readings were synchronized with a reference ground reaction force system to determine initial/terminal contacts. Then, we extracted a set of features from windowed data labeled according to the reference. Two gray-box approaches were evaluated: (1) classifiers (decision trees) returning the presence of a gait event in each time window and (2) a classifier discriminating between stance and swing phases. Both outputs were submitted to a deterministic algorithm correcting spurious clusters of predictions. The stance vs. swing approach estimated the stride time duration with an average error lower than 20 ms and confidence bounds between ±50 ms. These figures are suitable to detect clinically meaningful differences across different populations. Full article
(This article belongs to the Special Issue Wearables for Movement Analysis in Healthcare)
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23 pages, 3902 KiB  
Article
Evaluation and Application of a Customizable Wireless Platform: A Body Sensor Network for Unobtrusive Gait Analysis in Everyday Life
by Markus Lueken, Leo Mueller, Michel G. Decker, Cornelius Bollheimer, Steffen Leonhardt and Chuong Ngo
Sensors 2020, 20(24), 7325; https://0-doi-org.brum.beds.ac.uk/10.3390/s20247325 - 20 Dec 2020
Cited by 8 | Viewed by 2510
Abstract
Body sensor networks (BSNs) represent an important research tool for exploring novel diagnostic or therapeutic approaches. They allow for integrating different measurement techniques into body-worn sensors organized in a network structure. In 2011, the first Integrated Posture and Activity Network by MedIT Aachen [...] Read more.
Body sensor networks (BSNs) represent an important research tool for exploring novel diagnostic or therapeutic approaches. They allow for integrating different measurement techniques into body-worn sensors organized in a network structure. In 2011, the first Integrated Posture and Activity Network by MedIT Aachen (IPANEMA) was introduced. In this work, we present a recently developed platform for a wireless body sensor network with customizable applications based on a proprietary 868MHz communication interface. In particular, we present a sensor setup for gait analysis during everyday life monitoring. The arrangement consists of three identical inertial measurement sensors attached at the wrist, thigh, and chest. We additionally introduce a force-sensitive resistor integrated insole for measurement of ground reaction forces (GRFs), to enhance the assessment possibilities and generate ground truth data for inertial measurement sensors. Since the 868MHz is not strongly represented in existing BSN implementations, we validate the proposed system concerning an application in gait analysis and use this as a representative demonstration of realizability. Hence, there are three key aspects of this project. The system is evaluated with respect to (I) accurate timing, (II) received signal quality, and (III) measurement capabilities of the insole pressure nodes. In addition to the demonstration of feasibility, we achieved promising results regarding the extractions of gait parameters (stride detection accuracy: 99.6±0.8%, Root-Mean-Square Deviation (RMSE) of mean stride time: 5ms, RMSE of percentage stance time: 2.3%). Conclusion: With the satisfactory technical performance in laboratory and application environment and the convincing accuracy of the gait parameter extraction, the presented system offers a solid basis for a gait monitoring system in everyday life. Full article
(This article belongs to the Special Issue Wearables for Movement Analysis in Healthcare)
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20 pages, 10448 KiB  
Article
Design and Validation of an E-Textile-Based Wearable Sock for Remote Gait and Postural Assessment
by Federica Amitrano, Armando Coccia, Carlo Ricciardi, Leandro Donisi, Giuseppe Cesarelli, Edda Maria Capodaglio and Giovanni D’Addio
Sensors 2020, 20(22), 6691; https://0-doi-org.brum.beds.ac.uk/10.3390/s20226691 - 23 Nov 2020
Cited by 30 | Viewed by 4222
Abstract
This paper presents a new wearable e-textile based system, named SWEET Sock, for biomedical signals remote monitoring. The system includes a textile sensing sock, an electronic unit for data transmission, a custom-made Android application for real-time signal visualization, and a software desktop for [...] Read more.
This paper presents a new wearable e-textile based system, named SWEET Sock, for biomedical signals remote monitoring. The system includes a textile sensing sock, an electronic unit for data transmission, a custom-made Android application for real-time signal visualization, and a software desktop for advanced digital signal processing. The device allows the acquisition of angular velocities of the lower limbs and plantar pressure signals, which are postprocessed to have a complete and schematic overview of patient’s clinical status, regarding gait and postural assessment. In this work, device performances are validated by evaluating the agreement between the prototype and an optoelectronic system for gait analysis on a set of free walk acquisitions. Results show good agreement between the systems in the assessment of gait cycle time and cadence, while the presence of systematic and proportional errors are pointed out for swing and stance time parameters. Worse results were obtained in the comparison of spatial metrics. The “wearability” of the system and its comfortable use make it suitable to be used in domestic environment for the continuous remote health monitoring of de-hospitalized patients but also in the ergonomic assessment of health workers, thanks to its low invasiveness. Full article
(This article belongs to the Special Issue Wearables for Movement Analysis in Healthcare)
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22 pages, 2452 KiB  
Article
Assessment of Upper Limb Movement Impairments after Stroke Using Wearable Inertial Sensing
by Anne Schwarz, Miguel M. C. Bhagubai, Gerjan Wolterink, Jeremia P. O. Held, Andreas R. Luft and Peter H. Veltink
Sensors 2020, 20(17), 4770; https://0-doi-org.brum.beds.ac.uk/10.3390/s20174770 - 24 Aug 2020
Cited by 30 | Viewed by 5115
Abstract
Precise and objective assessments of upper limb movement quality after strokes in functional task conditions are an important prerequisite to improve understanding of the pathophysiology of movement deficits and to prove the effectiveness of interventions. Herein, a wearable inertial sensing system was used [...] Read more.
Precise and objective assessments of upper limb movement quality after strokes in functional task conditions are an important prerequisite to improve understanding of the pathophysiology of movement deficits and to prove the effectiveness of interventions. Herein, a wearable inertial sensing system was used to capture movements from the fingers to the trunk in 10 chronic stroke subjects when performing reach-to-grasp activities with the affected and non-affected upper limb. It was investigated whether the factors, tested arm, object weight, and target height, affect the expressions of range of motion in trunk compensation and flexion-extension of the elbow, wrist, and finger during object displacement. The relationship between these metrics and clinically measured impairment was explored. Nine subjects were included in the analysis, as one had to be excluded due to defective data. The tested arm and target height showed strong effects on all metrics, while an increased object weight showed effects on trunk compensation. High inter- and intrasubject variability was found in all metrics without clear relationships to clinical measures. Relating all metrics to each other resulted in significant negative correlations between trunk compensation and elbow flexion-extension in the affected arm. The findings support the clinical usability of sensor-based motion analysis. Full article
(This article belongs to the Special Issue Wearables for Movement Analysis in Healthcare)
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14 pages, 479 KiB  
Article
Smoothness of Gait in Healthy and Cognitively Impaired Individuals: A Study on Italian Elderly Using Wearable Inertial Sensor
by Massimiliano Pau, Ilaria Mulas, Valeria Putzu, Gesuina Asoni, Daniela Viale, Irene Mameli, Bruno Leban and Gilles Allali
Sensors 2020, 20(12), 3577; https://0-doi-org.brum.beds.ac.uk/10.3390/s20123577 - 24 Jun 2020
Cited by 19 | Viewed by 3466
Abstract
The main purpose of the present study was to compare the smoothness of gait in older adults with and without cognitive impairments, using the harmonic ratio (HR), a metric derived from trunk accelerations. Ninety older adults aged over 65 (age: 78.9 ± 4.8 [...] Read more.
The main purpose of the present study was to compare the smoothness of gait in older adults with and without cognitive impairments, using the harmonic ratio (HR), a metric derived from trunk accelerations. Ninety older adults aged over 65 (age: 78.9 ± 4.8 years; 62% female) underwent instrumental gait analysis, performed using a wearable inertial sensor and cognitive assessment with the Mini Mental State Examination (MMSE) and Addenbrooke’s Cognitive Examination Revised (ACE-R). They were stratified into three groups based on their MMSE performance: healthy controls (HC), early and advanced cognitive decline (ECD, ACD). The spatio-temporal and smoothness of gait parameters, the latter expressed through HR in anteroposterior (AP), vertical (V) and mediolateral (ML) directions, were derived from trunk acceleration data. The existence of a relationship between gait parameters and degree of cognitive impairment was also explored. The results show that individuals with ECD and ACD exhibited significantly slower speed and shorter stride length, as well as reduced values of HR in the AP and V directions compared to HC, while no significant differences were found between ECD and ACD in any of the investigated parameters. Gait speed, stride length and HR in all directions were found to be moderately correlated with both MMSE and ACE-R scores. Such findings suggest that, in addition to the known changes in gait speed and stride length, important reductions in smoothness of gait are likely to occur in older adults, owing to early/prodromal stages of cognitive impairment. Given the peculiar nature of these metrics, which refers to overall body stability during gait, the calculation of HR may result in being useful in improving the characterization of gait patterns in older adults with cognitive impairments. Full article
(This article belongs to the Special Issue Wearables for Movement Analysis in Healthcare)
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14 pages, 399 KiB  
Systematic Review
A Systematic Review of Diagnostic Accuracy and Clinical Applications of Wearable Movement Sensors for Knee Joint Rehabilitation
by Robert Prill, Marina Walter, Aleksandra Królikowska and Roland Becker
Sensors 2021, 21(24), 8221; https://0-doi-org.brum.beds.ac.uk/10.3390/s21248221 - 09 Dec 2021
Cited by 16 | Viewed by 2573
Abstract
In clinical practice, only a few reliable measurement instruments are available for monitoring knee joint rehabilitation. Advances to replace motion capturing with sensor data measurement have been made in the last years. Thus, a systematic review of the literature was performed, focusing on [...] Read more.
In clinical practice, only a few reliable measurement instruments are available for monitoring knee joint rehabilitation. Advances to replace motion capturing with sensor data measurement have been made in the last years. Thus, a systematic review of the literature was performed, focusing on the implementation, diagnostic accuracy, and facilitators and barriers of integrating wearable sensor technology in clinical practices based on a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. For critical appraisal, the COSMIN Risk of Bias tool for reliability and measurement of error was used. PUBMED, Prospero, Cochrane database, and EMBASE were searched for eligible studies. Six studies reporting reliability aspects in using wearable sensor technology at any point after knee surgery in humans were included. All studies reported excellent results with high reliability coefficients, high limits of agreement, or a few detectable errors. They used different or partly inappropriate methods for estimating reliability or missed reporting essential information. Therefore, a moderate risk of bias must be considered. Further quality criterion studies in clinical settings are needed to synthesize the evidence for providing transparent recommendations for the clinical use of wearable movement sensors in knee joint rehabilitation. Full article
(This article belongs to the Special Issue Wearables for Movement Analysis in Healthcare)
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