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Wearable Sensors for Gait, Human Motion Analysis and Health Monitoring

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

Deadline for manuscript submissions: 15 September 2024 | Viewed by 14098

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Special Issue Editors

Dr. Alessandro Leone

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Guest Editor

National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy
Interests: ambient assisted living; active&healthy ageing technologies; signal processing; image processing; computer vision
Special Issues, Collections and Topics in MDPI journals

Dr. Gabriele Rescio

E-Mail Website
Guest Editor

Institute for Microelectronics and Microsystems, National Research Council of Italy, 73100 Lecce, Italy
Interests: ambient assisted living; active & healthy ageing technologies; wearable sensors; signal processing; artificial intelligence

Special Issue Information

Dear Colleagues,

Human motion analysis and gait analysis have been traditionally performed in laboratories under controlled conditions using expensive equipment. Wearable sensors present an easy-to-use and cheap way to perform both gait and human motion analysis, including in healthcare scenarios where monitoring is critically important.

Wearable sensors are an increasingly popular approach to the quantification of performance and workload with mechanical and physiological parameters. A wide range of wearable sensors are commercially available, and, when applied to gait analysis or motion analysis, they can provide kinetic and kinematic features, thus representing useful tools for clinicians, researchers and caregivers in real-life contexts.

Wearable smart devices and services can be applied in microelectronics, new sensing technologies and materials, transducers, signal processing, big data, cloud computing and artificial intelligent, making them attractive in biomechanics contexts for real-life and real-time analysis.

The Special Issue refers to the design, implementation, testing, benchmarking and use of wearable sensors and related infrastructures and services, including ambient assisted living, ambient intelligence and IoT paradigms, and reframing the sense of “Smart Living” to ensure inclusion, safety, comfort, care, healthcare and environmental sustainability. The Special Issue aims to cover technological issues related to the integration of hardware and processing aspects in wearable smart devices for motion analysis and health monitoring, including mobile, edge, fog and cloud computing.

We invite papers that include, but not exclusively, the following topics:

Posture and gait analysis;
Human daily motion analysis;
Gait analysis of elderly and disabled people;
Home care motion sensing and analysis;
Wearable sensors and related techniques for medical decision making;
Wearable sensors and related techniques for motor diagnosis;
Wearable sensors and related techniques for human gait recognition;
Sensing technologies for ambulatory human motion analysis;
Advanced sensor signal processing;
Health monitoring systems;
Industry-related wearable sensors;
Innovative applications of wearable sensor systems.

Dr. Alessandro Leone
Dr. Gabriele Rescio
Guest Editors

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.

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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.

Published Papers (10 papers)

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Research

17 pages, 10433 KiB

Open AccessArticle

Choice of Piezoelectric Element over Accelerometer for an Energy-Autonomous Shoe-Based System

by Niharika Gogoi, Yuanjia Zhu, Jens Kirchner and Georg Fischer

Sensors 2024, 24(8), 2549; https://doi.org/10.3390/s24082549 - 16 Apr 2024

Viewed by 453

Abstract

Shoe-based wearable sensor systems are a growing research area in health monitoring, disease diagnosis, rehabilitation, and sports training. These systems—equipped with one or more sensors, either of the same or different types—capture information related to foot movement or pressure maps beneath the foot. This captured information offers an overview of the subject’s overall movement, known as the human gait. Beyond sensing, these systems also provide a platform for hosting ambient energy harvesters. They hold the potential to harvest energy from foot movements and operate related low-power devices sustainably. This article proposes two types of strategies (Strategy 1 and Strategy 2) for an energy-autonomous shoe-based system. Strategy 1 uses an accelerometer as a sensor for gait acquisition, which reflects the classical choice. Strategy 2 uses a piezoelectric element for the same, which opens up a new perspective in its implementation. In both strategies, the piezoelectric elements are used to harvest energy from foot activities and operate the system. The article presents a fair comparison between both strategies in terms of power consumption, accuracy, and the extent to which piezoelectric energy harvesters can contribute to overall power management. Moreover, Strategy 2, which uses piezoelectric elements for simultaneous sensing and energy harvesting, is a power-optimized method for an energy-autonomous shoe system. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait, Human Motion Analysis and Health Monitoring)

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13 pages, 7878 KiB

Open AccessArticle

Aluminum Nitride Thin Film Piezoelectric Pressure Sensor for Respiratory Rate Detection

by Maria Assunta Signore, Gabriele Rescio, Luca Francioso, Flavio Casino and Alessandro Leone

Sensors 2024, 24(7), 2071; https://doi.org/10.3390/s24072071 - 24 Mar 2024

Viewed by 492

Abstract

In this study, we propose a low-cost piezoelectric flexible pressure sensor fabricated on Kapton^® (Kapton™ Dupont) substrate by using aluminum nitride (AlN) thin film, designed for the monitoring of the respiration rate for a fast detection of respiratory anomalies. The device was characterized in the range of 15–30 breaths per minute (bpm), to simulate moderate difficult breathing, borderline normal breathing, and normal spontaneous breathing. These three breathing typologies were artificially reproduced by setting the expiratory to inspiratory ratios (E:I) at 1:1, 2:1, 3:1. The prototype was able to accurately recognize the breath states with a low response time (~35 ms), excellent linearity (R² = 0.997) and low hysteresis. The piezoelectric device was also characterized by placing it in an activated carbon filter mask to evaluate the pressure generated by exhaled air through breathing acts. The results indicate suitability also for the monitoring of very weak breath, exhibiting good linearity, accuracy, and reproducibility, in very low breath pressures, ranging from 0.09 to 0.16 kPa. These preliminary results are very promising for the future development of smart wearable devices able to monitor different patients breathing patterns, also related to breathing diseases, providing a suitable real-time diagnosis in a non-invasive and fast way. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait, Human Motion Analysis and Health Monitoring)

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25 pages, 10721 KiB

Open AccessArticle

Estimating Compressive and Shear Forces at L5-S1: Exploring the Effects of Load Weight, Asymmetry, and Height Using Optical and Inertial Motion Capture Systems

by Iván Nail-Ulloa, Michael Zabala, Richard Sesek, Howard Chen, Mark C. Schall, Jr. and Sean Gallagher

Sensors 2024, 24(6), 1941; https://doi.org/10.3390/s24061941 - 18 Mar 2024

Viewed by 801

Abstract

This study assesses the agreement of compressive and shear force estimates at the L5-S1 joint using inertial motion capture (IMC) within a musculoskeletal simulation model during manual lifting tasks, compared against a top-down optical motion capture (OMC)-based model. Thirty-six participants completed lifting and lowering tasks while wearing a modified Plug-in Gait marker set for the OMC and a full-body IMC set-up consisting of 17 sensors. The study focused on tasks with variable load weights, lifting heights, and trunk rotation angles. It was found that the IMC system consistently underestimated the compressive forces by an average of 34% (975.16 N) and the shear forces by 30% (291.77 N) compared with the OMC system. A critical observation was the discrepancy in joint angle measurements, particularly in trunk flexion, where the IMC-based model underestimated the angles by 10.92–11.19 degrees on average, with the extremes reaching up to 28 degrees. This underestimation was more pronounced in tasks involving greater flexion, notably impacting the force estimates. Additionally, this study highlights significant differences in the distance from the spine to the box during these tasks. On average, the IMC system showed an 8 cm shorter distance on the X axis and a 12–13 cm shorter distance on the Z axis during lifting and lowering, respectively, indicating a consistent underestimation of the segment length compared with the OMC system. These discrepancies in the joint angles and distances suggest potential limitations of the IMC system’s sensor placement and model scaling. The load weight emerged as the most significant factor affecting force estimates, particularly at lower lifting heights, which involved more pronounced flexion movements. This study concludes that while the IMC system offers utility in ergonomic assessments, sensor placement and anthropometric modeling accuracy enhancements are imperative for more reliable force and kinematic estimations in occupational settings. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait, Human Motion Analysis and Health Monitoring)

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14 pages, 8171 KiB

Open AccessArticle

A Deep Learning-Based Platform for Workers’ Stress Detection Using Minimally Intrusive Multisensory Devices

by Gabriele Rescio, Andrea Manni, Marianna Ciccarelli, Alessandra Papetti, Andrea Caroppo and Alessandro Leone

Sensors 2024, 24(3), 947; https://doi.org/10.3390/s24030947 - 01 Feb 2024

Viewed by 760

Abstract

The advent of Industry 4.0 necessitates substantial interaction between humans and machines, presenting new challenges when it comes to evaluating the stress levels of workers who operate in increasingly intricate work environments. Undoubtedly, work-related stress exerts a significant influence on individuals’ overall stress levels, leading to enduring health issues and adverse impacts on their quality of life. Although psychological questionnaires have traditionally been employed to assess stress, they lack the capability to monitor stress levels in real-time or on an ongoing basis, thus making it arduous to identify the causes and demanding aspects of work. To surmount this limitation, an effective solution lies in the analysis of physiological signals that can be continuously measured through wearable or ambient sensors. Previous studies in this field have mainly focused on stress assessment through intrusive wearable systems susceptible to noise and artifacts that degrade performance. One of our recently published papers presented a wearable and ambient hardware-software platform that is minimally intrusive, able to detect human stress without hindering normal work activities, and slightly susceptible to artifacts due to movements. A limitation of this system is its not very high performance in terms of the accuracy of detecting multiple stress levels; therefore, in this work, the focus was on improving the software performance of the platform, using a deep learning approach. To this purpose, three neural networks were implemented, and the best performance was achieved by the 1D-convolutional neural network with an accuracy of 95.38% for the identification of two levels of stress, which is a significant improvement over those obtained previously. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait, Human Motion Analysis and Health Monitoring)

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15 pages, 7722 KiB

Open AccessArticle

Assessing Spatiotemporal and Quality Alterations in Paretic Upper Limb Movements after Stroke in Routine Care: Proposal and Validation of a Protocol Using IMUs versus MoCap

by Baptiste Merlau, Camille Cormier, Alexia Alaux, Margot Morin, Emmeline Montané, David Amarantini and David Gasq

Sensors 2023, 23(17), 7427; https://doi.org/10.3390/s23177427 - 25 Aug 2023

Cited by 2 | Viewed by 840

Abstract

Accurate assessment of upper-limb movement alterations is a key component of post-stroke follow-up. Motion capture (MoCap) is the gold standard for assessment even in clinical conditions, but it requires a laboratory setting with a relatively complex implementation. Alternatively, inertial measurement units (IMUs) are the subject of growing interest, but their accuracy remains to be challenged. This study aims to assess the minimal detectable change (MDC) between spatiotemporal and quality variables obtained from these IMUs and MoCap, based on a specific protocol of IMU calibration and measurement and on data processing using the dead reckoning method. We also studied the influence of each data processing step on the level of between-system MDC. Fifteen post-stroke hemiparetic subjects performed reach or grasp tasks. The MDC for the movement time, index of curvature, smoothness (studied through the number of submovements), and trunk contribution was equal to 10.83%, 3.62%, 39.62%, and 25.11%, respectively. All calibration and data processing steps played a significant role in increasing the agreement. The between-system MDC values were found to be lower or comparable to the between-session MDC values obtained with MoCap, meaning that our results provide strong evidence that using IMUs with the proposed calibration and processing steps can successfully and accurately assess upper-limb movement alterations after stroke in clinical routine care conditions. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait, Human Motion Analysis and Health Monitoring)

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13 pages, 3225 KiB

Open AccessArticle

Gait Stride Length Estimation Using Embedded Machine Learning

by Joeri R. Verbiest, Bruno Bonnechère, Wim Saeys, Patricia Van de Walle, Steven Truijen and Pieter Meyns

Sensors 2023, 23(16), 7166; https://doi.org/10.3390/s23167166 - 14 Aug 2023

Cited by 2 | Viewed by 1410

Abstract

Introduction. Spatiotemporal gait parameters, e.g., gait stride length, are measurements that are classically derived from instrumented gait analysis. Today, different solutions are available for gait assessment outside the laboratory, specifically for spatiotemporal gait parameters. Such solutions are wearable devices that comprise an inertial measurement unit (IMU) sensor and a microcontroller (MCU). However, these existing wearable devices are resource-constrained. They contain a processing unit with limited processing and memory capabilities which limit the use of machine learning to estimate spatiotemporal gait parameters directly on the device. The solution for this limitation is embedded machine learning or tiny machine learning (tinyML). This study aims to create a machine-learning model for gait stride length estimation deployable on a microcontroller. Materials and Method. Starting from a dataset consisting of 4467 gait strides from 15 healthy people, measured by IMU sensor, and using state-of-the-art machine learning frameworks and machine learning operations (MLOps) tools, a multilayer 1D convolutional float32 and int8 model for gait stride length estimation was developed. Results. The developed float32 model demonstrated a mean accuracy and precision of 0.23 ± 4.3 cm, and the int8 model demonstrated a mean accuracy and precision of 0.07 ± 4.3 cm. The memory usage for the float32 model was 284.5 kB flash and 31.9 kB RAM. The int8 model memory usage was 91.6 kB flash and 13.6 kB RAM. Both models were able to be deployed on a Cortex-M4F 64 MHz microcontroller with 1 MB flash memory and 256 kB RAM. Conclusions. This study shows that estimating gait stride length directly on a microcontroller is feasible and demonstrates the potential of embedded machine learning, or tinyML, in designing wearable sensor devices for gait analysis. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait, Human Motion Analysis and Health Monitoring)

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15 pages, 1730 KiB

Open AccessArticle

Validity and Reliability of a Wearable Goniometer Sensor Controlled by a Mobile Application for Measuring Knee Flexion/Extension Angle during the Gait Cycle

by Tomoya Ishida and Mina Samukawa

Sensors 2023, 23(6), 3266; https://doi.org/10.3390/s23063266 - 20 Mar 2023

Cited by 2 | Viewed by 2102

Abstract

Knee kinematics during gait is an important assessment tool in health-promotion and clinical fields. This study aimed to determine the validity and reliability of a wearable goniometer sensor for measuring knee flexion angles throughout the gait cycle. Twenty-two and seventeen participants were enrolled in the validation and reliability study, respectively. The knee flexion angle during gait was assessed using a wearable goniometer sensor and a standard optical motion analysis system. The coefficient of multiple correlation (CMC) between the two measurement systems was 0.992 ± 0.008. Absolute error (AE) was 3.3 ± 1.5° (range: 1.3–6.2°) for the entire gait cycle. An acceptable AE (<5°) was observed during 0–65% and 87–100% of the gait cycle. Discrete analysis revealed a significant correlation between the two systems (R = 0.608–0.904, p ≤ 0.001). The CMC between the two measurement days with a 1-week interval was 0.988 ± 0.024, and the AE was 2.5 ± 1.2° (range: 1.1–4.5°). A good-to-acceptable AE (<5°) was observed throughout the gait cycle. These results indicate that the wearable goniometer sensor is useful for assessing knee flexion angle during the stance phase of the gait cycle. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait, Human Motion Analysis and Health Monitoring)

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17 pages, 5746 KiB

Open AccessArticle

Motor Overflow during Reaching in Infancy: Quantification of Limb Movement Using Inertial Motion Units

by Agata Kozioł, David López Pérez, Zuzanna Laudańska, Anna Malinowska-Korczak, Karolina Babis, Oleksandra Mykhailova, Hana D’Souza and Przemysław Tomalski

Sensors 2023, 23(5), 2653; https://doi.org/10.3390/s23052653 - 28 Feb 2023

Viewed by 1591

Abstract

Early in life, infants exhibit motor overflow, which can be defined as the generation of involuntary movements accompanying purposeful actions. We present the results of a quantitative study exploring motor overflow in 4-month-old infants. This is the first study quantifying motor overflow with high accuracy and precision provided by Inertial Motion Units. The study aimed to investigate the motor activity across the non-acting limbs during goal-directed action. To this end, we used wearable motion trackers to measure infant motor activity during a baby-gym task designed to capture overflow during reaching movements. The analysis was conducted on the subsample of participants (n = 20), who performed at least four reaches during the task. A series of Granger causality tests revealed that the activity differed depending on the non-acting limb and the type of the reaching movement. Importantly, on average, the non-acting arm preceded the activation of the acting arm. In contrast, the activity of the acting arm was followed by the activation of the legs. This may be caused by their distinct purposes in supporting postural stability and efficiency of movement execution. Finally, our findings demonstrate the utility of wearable motion trackers for precise measurement of infant movement dynamics. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait, Human Motion Analysis and Health Monitoring)

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11 pages, 1628 KiB

Open AccessArticle

Validation of an Ear-Worn Wearable Gait Analysis Device

by Chang Keun Jung, Jinkyuk Kim and Hye Chang Rhim

Sensors 2023, 23(3), 1244; https://doi.org/10.3390/s23031244 - 21 Jan 2023

Cited by 2 | Viewed by 2661

Abstract

Wearable devices capable of measuring gait parameters may provide a means to more economical gait analysis compared to conventional equipment comprising of a motion capture system and a forced treadmill. Beflex Coach (Beflex, Republic of Korea) is one such device but worn on the ear as Bluetooth earphones, unlike other wearables worn on the wrist, feet, or torso. In this study, the validity of the device was examined against a motion capture system and a forced treadmill for walking and running parameters. Five walking parameters (cadence, single support time, double support time, vertical oscillation (VO), and instantaneous vertical loading rate (IVLR)) and six running parameters (cadence, stance time, flight time, peak force, VO, and IVLR) were studied. Twenty young adults participated in walking or running on a forced treadmill at different speeds (walking: 0.8, 1.25, and 1.7 m/s for walking; running: 2, 2.5, and 3 m/s) while the two systems operated simultaneously. As a result, all parameters showed excellent associations (ICC > 0.75) and good agreements in Bland–Altman plots. The results of the study support the potential use of the ear-worn device as an inexpensive gait analysis equipment. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait, Human Motion Analysis and Health Monitoring)

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12 pages, 985 KiB

Open AccessArticle

Step-Counting Accuracy of a Commercial Smartwatch in Mild-to-Moderate PD Patients and Effect of Spatiotemporal Gait Parameters, Laterality of Symptoms, Pharmacological State, and Clinical Variables

by Edoardo Bianchini, Bianca Caliò, Marika Alborghetti, Domiziana Rinaldi, Clint Hansen, Nicolas Vuillerme, Walter Maetzler and Francesco E. Pontieri

Sensors 2023, 23(1), 214; https://doi.org/10.3390/s23010214 - 25 Dec 2022

Cited by 2 | Viewed by 1706

Abstract

Commercial smartwatches could be useful for step counting and monitoring ambulatory activity. However, in Parkinson’s disease (PD) patients, an altered gait, pharmacological condition, and symptoms lateralization may affect their accuracy and potential usefulness in research and clinical routine. Steps were counted during a 6 min walk in 47 patients with PD and 47 healthy subjects (HS) wearing a Garmin Vivosmart 4 (GV4) on each wrist. Manual step counting was used as a reference. An inertial sensor (BTS G-Walk), placed on the lower back, was used to compute spatial-temporal gait parameters. Intraclass correlation coefficient (ICC) and mean absolute percentage error (MAPE) were used for accuracy evaluation and the Spearman test was used to assess the correlations between variables. The GV4 overestimated steps in PD patients with only a poor-to-moderate agreement. The OFF pharmacological state and wearing the device on the most-affected body side led to an unacceptable accuracy. The GV4 showed an excellent agreement and MAPE in HS at a self-selected speed, but an unacceptable performance at a slow speed. In PD patients, MAPE was not associated with gait parameters and clinical variables. The accuracy of commercial smartwatches for monitoring step counting might be reduced in PD patients and further influenced by the pharmacological condition and placement of the device. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait, Human Motion Analysis and Health Monitoring)

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