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

Deadline for manuscript submissions: closed (31 January 2021) | Viewed by 38202

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Special Issue Editor

Prof. Dr. Jochen Klenk

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Guest Editor

1. Institute of Epidemiology and Medical Biometry; Ulm University; 89075 Ulm, Germany
2. Department of Clinical Gerontology; Robert Bosch Hospital; 70376 Stuttgart, Germany
3. IB University of Applied Sciences Berlin; Study centre Stuttgart; 70178 Stuttgart, Germany
Interests: physical activity; falls; wearable sensors; reactive balance control and perturbation training; older people; trajectory analyses

Special Issue Information

Dear colleagues,

Wearable inertial sensors, including accelerometers, gyroscopes, and magnetometers, have developed considerably during the last decade. Through the progress of performance, miniaturization, and drop in costs, wearable inertial sensors have been integrated in several products of daily life, such as smartphones and smart watches.

The fields of application are numerous. For example, activity monitoring and fitness tracking are widely used in the consumer market. In sports science and biomechanics, wearable inertial sensors enable mobile human motion analysis outside the laboratory. Various medical applications based on signal data from wearable inertial sensors have been developed, including fall risk assessment, fall detection, and early identification of Parkinson’s disease. Moreover, wearable inertial sensors are essential for mixed reality smart glasses devices and head-mounted displays.

This Special Issue aims to collect new developments and research results in the broad field of wearable inertial sensors. The topics for this Special Issue include but are not limited to:

Basic Technologies for wearable inertial sensors:

Accelerometers
Gyroscopes
Magnetometers
Sensor networks
Sensor fusion
Signal processing algorithms including artificial intelligence

Applications:

Motion analysis and biomechanics
Health monitoring
Navigation and tracking
Biofeedback
Human machine interfaces
New and unconventional applications of wearable inertial sensors

Prof. Dr. Jochen Klenk
Guest Editor

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Keywords

Wearable inertial sensors
Accelerometers
Gyroscopes
Magnetometers
MEMS sensors
Sensor fusion
Navigation
Tracking
Motion analysis
Signal processing
Algorithms

Published Papers (10 papers)

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Research

14 pages, 2605 KiB

Open AccessArticle

A Cost-Effective Inertial Measurement System for Tracking Movement and Triggering Kinesthetic Feedback in Lower-Limb Prosthesis Users

by McNiel-Inyani Keri, Ahmed W. Shehata, Paul D. Marasco, Jacqueline S. Hebert and Albert H. Vette

Sensors 2021, 21(5), 1844; https://0-doi-org.brum.beds.ac.uk/10.3390/s21051844 - 06 Mar 2021

Cited by 6 | Viewed by 3044

Abstract

Advances in lower-limb prosthetic technologies have facilitated the restoration of ambulation; however, users of such technologies still experience reduced balance control, also due to the absence of proprioceptive feedback. Recent efforts have demonstrated the ability to restore kinesthetic feedback in upper-limb prosthesis applications; however, technical solutions to trigger the required muscle vibration and provide automated feedback have not been explored for lower-limb prostheses. The study’s first objective was therefore to develop a feedback system capable of tracking lower-limb movement and automatically triggering a muscle vibrator to induce the kinesthetic illusion. The second objective was to investigate the developed system’s ability to provide kinesthetic feedback in a case participant. A low-cost, wireless feedback system, incorporating two inertial measurement units to trigger a muscle vibrator, was developed and tested in an individual with limb loss above the knee. Our system had a maximum communication delay of 50 ms and showed good tracking of Gaussian and sinusoidal movement profiles for velocities below 180 degrees per second (error < 8 degrees), mimicking stepping and walking, respectively. We demonstrated in the case participant that the developed feedback system can successfully elicit the kinesthetic illusion. Our work contributes to the integration of sensory feedback in lower-limb prostheses, to increase their use and functionality. Full article

(This article belongs to the Special Issue Wearable Inertial Sensors)

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22 pages, 4745 KiB

Open AccessArticle

Automatic Functional Shoulder Task Identification and Sub-Task Segmentation Using Wearable Inertial Measurement Units for Frozen Shoulder Assessment

by Chih-Ya Chang, Chia-Yeh Hsieh, Hsiang-Yun Huang, Yung-Tsan Wu, Liang-Cheng Chen, Chia-Tai Chan and Kai-Chun Liu

Sensors 2021, 21(1), 106; https://0-doi-org.brum.beds.ac.uk/10.3390/s21010106 - 26 Dec 2020

Cited by 6 | Viewed by 2459

Abstract

Advanced sensor technologies have been applied to support frozen shoulder assessment. Sensor-based assessment tools provide objective, continuous and quantitative information for evaluation and diagnosis. However, the current tools for assessment of functional shoulder tasks mainly rely on manual operation. It may cause several technical issues to the reliability and usability of the assessment tool, including manual bias during the recording and additional efforts for data labeling. To tackle these issues, this pilot study aims to propose an automatic functional shoulder task identification and sub-task segmentation system using inertial measurement units to provide reliable shoulder task labeling and sub-task information for clinical professionals. The proposed method combines machine learning models and rule-based modification to identify shoulder tasks and segment sub-tasks accurately. A hierarchical design is applied to enhance the efficiency and performance of the proposed approach. Nine healthy subjects and nine frozen shoulder patients are invited to perform five common shoulder tasks in the lab-based and clinical environments, respectively. The experimental results show that the proposed method can achieve 87.11% F-score for shoulder task identification, and 83.23% F-score and 427 mean absolute time errors (milliseconds) for sub-task segmentation. The proposed approach demonstrates the feasibility of the proposed method to support reliable evaluation for clinical assessment. Full article

(This article belongs to the Special Issue Wearable Inertial Sensors)

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17 pages, 4202 KiB

Open AccessArticle

Fluid Intake Monitoring System Using a Wearable Inertial Sensor for Fluid Intake Management

by Hsiang-Yun Huang, Chia-Yeh Hsieh, Kai-Chun Liu, Steen Jun-Ping Hsu and Chia-Tai Chan

Sensors 2020, 20(22), 6682; https://0-doi-org.brum.beds.ac.uk/10.3390/s20226682 - 22 Nov 2020

Cited by 12 | Viewed by 3779

Abstract

Fluid intake is important for people to maintain body fluid homeostasis. Inadequate fluid intake leads to negative health consequences, such as headache, dizziness and urolithiasis. However, people in busy lifestyles usually forget to drink sufficient water and neglect the importance of fluid intake. Fluid intake management is important to assist people in adopting individual drinking behaviors. This work aims to propose a fluid intake monitoring system with a wearable inertial sensor using a hierarchical approach to detect drinking activities, recognize sip gestures and estimate fluid intake amount. Additionally, container-dependent amount estimation models are developed due to the influence of containers on fluid intake amount. The proposed fluid intake monitoring system could achieve 94.42% accuracy, 90.17% sensitivity, and 40.11% mean absolute percentage error (MAPE) for drinking detection, gesture spotting and amount estimation, respectively. Particularly, MAPE of amount estimation is improved approximately 10% compared to the typical approaches. The results have demonstrated the feasibility and the effectiveness of the proposed fluid intake monitoring system. Full article

(This article belongs to the Special Issue Wearable Inertial Sensors)

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15 pages, 1309 KiB

Open AccessArticle

Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls

by Luca Palmerini, Jochen Klenk, Clemens Becker and Lorenzo Chiari

Sensors 2020, 20(22), 6479; https://0-doi-org.brum.beds.ac.uk/10.3390/s20226479 - 13 Nov 2020

Cited by 33 | Viewed by 5508

Abstract

Falling is a significant health problem. Fall detection, to alert for medical attention, has been gaining increasing attention. Still, most of the existing studies use falls simulated in a laboratory environment to test the obtained performance. We analyzed the acceleration signals recorded by an inertial sensor on the lower back during 143 real-world falls (the most extensive collection to date) from the FARSEEING repository. Such data were obtained from continuous real-world monitoring of subjects with a moderate-to-high risk of falling. We designed and tested fall detection algorithms using features inspired by a multiphase fall model and a machine learning approach. The obtained results suggest that algorithms can learn effectively from features extracted from a multiphase fall model, consistently overperforming more conventional features. The most promising method (support vector machines and features from the multiphase fall model) obtained a sensitivity higher than 80%, a false alarm rate per hour of 0.56, and an F-measure of 64.6%. The reported results and methodologies represent an advancement of knowledge on real-world fall detection and suggest useful metrics for characterizing fall detection systems for real-world use. Full article

(This article belongs to the Special Issue Wearable Inertial Sensors)

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12 pages, 3101 KiB

Open AccessArticle

Quantifying Reliable Walking Activity with a Wearable Device in Aged Residential Care: How Many Days Are Enough?

by Christopher Buckley, Alana Cavadino, Silvia Del Din, Sue Lord, Lynne Taylor, Lynn Rochester and Ngaire Kerse

Sensors 2020, 20(21), 6314; https://0-doi-org.brum.beds.ac.uk/10.3390/s20216314 - 05 Nov 2020

Cited by 7 | Viewed by 2342

Abstract

Strong associations exist between quality of life and physical activity for those living in aged residential care (ARC). Suitable and reliable tools are required to quantify physical activity for descriptive and evaluative purposes. We calculated the number of days required for reliable walking outcomes indicative of physical activity in an ARC population using a trunk-worn device. ARC participants (n = 257) wore the device for up to 7 days. Reasons for data loss were also recorded. The volume, pattern, and variability of walking was calculated. For 197 participants who wore the device for at least 3 days, linear mixed models determined the impact of week structure and number of days required to achieve reliable outcomes, collectively and then stratified by care level. The average days recorded by the wearable device was 5.2 days. Day of the week did not impact walking activity. Depending on the outcome and level of care, 2–5 days was sufficient for reliable estimates. This study provides informative evidence for future studies aiming to use a wearable device located on the trunk to quantify physical activity walking out in the ARC population. Full article

(This article belongs to the Special Issue Wearable Inertial Sensors)

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17 pages, 1610 KiB

Open AccessArticle

Gait Characteristics Harvested during a Smartphone-Based Self-Administered 2-Minute Walk Test in People with Multiple Sclerosis: Test-Retest Reliability and Minimum Detectable Change

by Alan K. Bourke, Alf Scotland, Florian Lipsmeier, Christian Gossens and Michael Lindemann

Sensors 2020, 20(20), 5906; https://0-doi-org.brum.beds.ac.uk/10.3390/s20205906 - 19 Oct 2020

Cited by 19 | Viewed by 5085

Abstract

The measurement of gait characteristics during a self-administered 2-minute walk test (2MWT), in persons with multiple sclerosis (PwMS), using a single body-worn device, has the potential to provide high-density longitudinal information on disease progression, beyond what is currently measured in the clinician-administered 2MWT. The purpose of this study is to determine the test-retest reliability, standard error of measurement (SEM) and minimum detectable change (MDC) of features calculated on gait characteristics, harvested during a self-administered 2MWT in a home environment, in 51 PwMS and 11 healthy control (HC) subjects over 24 weeks, using a single waist-worn inertial sensor-based smartphone. Excellent, or good to excellent test-retest reliability were observed in 58 of the 92 temporal, spatial and spatiotemporal gait features in PwMS. However, these were less reliable for HCs. Low SEM% and MDC% values were observed for most of the distribution measures for all gait characteristics for PwMS and HCs. This study demonstrates the inter-session test-retest reliability and provides an indication of clinically important change estimates, for interpreting the outcomes of gait characteristics measured using a body-worn smartphone, during a self-administered 2MWT. This system thus provides a reliable measure of gait characteristics in PwMS, supporting its application for the longitudinal assessment of gait deficits in this population. Full article

(This article belongs to the Special Issue Wearable Inertial Sensors)

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13 pages, 2743 KiB

Open AccessArticle

Detection of Pre-Impact Falls from Heights Using an Inertial Measurement Unit Sensor

by Youngho Kim, Haneul Jung, Bummo Koo, Jongman Kim, Taehee Kim and Yejin Nam

Sensors 2020, 20(18), 5388; https://0-doi-org.brum.beds.ac.uk/10.3390/s20185388 - 20 Sep 2020

Cited by 13 | Viewed by 3088

Abstract

Many safety accidents can occur in industrial sites. Among them, falls from heights (FFHs) are the most frequent accidents and have the highest fatality rate. Therefore, some existing studies have developed personal wearable airbags to mitigate the damage caused by FFHs. To utilize these airbags effectively, it is essential to detect FFHs before collision with the floor. In this study, an inertial measurement unit (IMU) sensor attached to the seventh thoracic vertebrae (T7) was used to develop an FFH detection algorithm. The vertical angle and vertical velocity were calculated using the inertial data obtained from the IMU sensor. Forty young and healthy males were recruited to perform non-FFH and FFH motions. In addition, experiments using a human mannequin and dynamics simulations were performed to obtain FFH data at heights above 2 m. The developed algorithm achieved 100% FFH detection accuracy and provided sufficient lead time such that the airbags could be inflated completely before collision with the floor. Full article

(This article belongs to the Special Issue Wearable Inertial Sensors)

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24 pages, 4596 KiB

Open AccessArticle

Turning Detection During Gait: Algorithm Validation and Influence of Sensor Location and Turning Characteristics in the Classification of Parkinson’s Disease

by Rana Zia Ur Rehman, Philipp Klocke, Sofia Hryniv, Brook Galna, Lynn Rochester, Silvia Del Din and Lisa Alcock

Sensors 2020, 20(18), 5377; https://0-doi-org.brum.beds.ac.uk/10.3390/s20185377 - 19 Sep 2020

Cited by 19 | Viewed by 5231

Abstract

Parkinson’s disease (PD) is a common neurodegenerative disorder resulting in a range of mobility deficits affecting gait, balance and turning. In this paper, we present: (i) the development and validation of an algorithm to detect turns during gait; (ii) a method to extract turn characteristics; and (iii) the classification of PD using turn characteristics. Thirty-seven people with PD and 56 controls performed 180-degree turns during an intermittent walking task. Inertial measurement units were attached to the head, neck, lower back and ankles. A turning detection algorithm was developed and validated by two raters using video data. Spatiotemporal and signal-based characteristics were extracted and used for PD classification. There was excellent absolute agreement between the rater and the algorithm for identifying turn start and end (ICC ≥ 0.99). Classification modeling (partial least square discriminant analysis (PLS-DA)) gave the best accuracy of 97.85% when trained on upper body and ankle data. Balanced sensitivity (97%) and specificity (96.43%) were achieved using turning characteristics from the neck, lower back and ankles. Turning characteristics, in particular angular velocity, duration, number of steps, jerk and root mean square distinguished mild-moderate PD from controls accurately and warrant future examination as a marker of mobility impairment and fall risk in PD. Full article

(This article belongs to the Special Issue Wearable Inertial Sensors)

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12 pages, 544 KiB

Open AccessArticle

Factors Associated with Callus Formation in the Plantar Region through Gait Measurement in Patients with Diabetic Neuropathy: An Observational Case-Control Study

by Ayumi Amemiya, Hiroshi Noguchi, Makoto Oe, Kimie Takehara, Yumiko Ohashi, Ryo Suzuki, Toshimasa Yamauchi, Takashi Kadowaki, Hiromi Sanada and Taketoshi Mori

Sensors 2020, 20(17), 4863; https://0-doi-org.brum.beds.ac.uk/10.3390/s20174863 - 28 Aug 2020

Cited by 4 | Viewed by 3730

Abstract

Callus has been identified as a risk factor leading to severe diabetic foot ulcer; thus, it is necessary to prevent its formation. Callus formation under the first, second, and fifth metatarsal heads (MTHs) is associated with external forces (pressure and shear stress) during walking. However, the gait factors increasing the external forces remain undetermined. Thus, this study aims to identify the factors increasing the external forces to prevent callus formation. In 59 patients with diabetic neuropathy wearing their usual shoes, the external forces, and the lower extremity joint angles were measured using MEMS force sensors and motion sensors. The external forces and their relationship with the lower extremity joint angles and footwear size were determined. Risk factors causing high external forces on the first MTH included small flexion of the knee joint (p = 0.015) and large ankle pronation motion (p = 0.034) to obtain propulsion. For the second MTH, wearing excessively long footwear was identified (p = 0.026). For the fifth MTH, high external force was related to tight width footwear (p = 0.005). An effective intervention for preventing callus formation for the first MTH would involve assisting the push-off foot motion using rocker-sole footwear or gait training. For the second and fifth MTHs, wearing appropriate size footwear would be effective. Full article

(This article belongs to the Special Issue Wearable Inertial Sensors)

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16 pages, 2418 KiB

Open AccessArticle

An Energy-Efficient Fall Detection Method Based on FD-DNN for Elderly People

by Leyuan Liu, Yibin Hou, Jian He, Jonathan Lungu and Ruihai Dong

Sensors 2020, 20(15), 4192; https://0-doi-org.brum.beds.ac.uk/10.3390/s20154192 - 28 Jul 2020

Cited by 13 | Viewed by 2680

Abstract

A fall detection module is an important component of community-based care for the elderly to reduce their health risk. It requires the accuracy of detections as well as maintains energy saving. In order to meet the above requirements, a sensing module-integrated energy-efficient sensor was developed which can sense and cache the data of human activity in sleep mode, and an interrupt-driven algorithm is proposed to transmit the data to a server integrated with ZigBee. Secondly, a deep neural network for fall detection (FD-DNN) running on the server is carefully designed to detect falls accurately. FD-DNN, which combines the convolutional neural networks (CNN) with long short-term memory (LSTM) algorithms, was tested on both with online and offline datasets. The experimental result shows that it takes advantage of CNN and LSTM, and achieved 99.17% fall detection accuracy, while its specificity and sensitivity are 99.94% and 94.09%, respectively. Meanwhile, it has the characteristics of low power consumption. Full article

(This article belongs to the Special Issue Wearable Inertial Sensors)

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