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Special Issue "Advances in Sensor Technologies for Wearable Applications"

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

Deadline for manuscript submissions: 25 November 2023 | Viewed by 6009

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Special Issue Editors

Prof. Dr. David T. Fullwood
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Guest Editor

Department of Mechanical Engineering, Brigham Young University, Provo, UT 84602, USA
Interests: nano-composites, microscopy and computational methods in materials science

Prof. Dr. Anton E. Bowden

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Guest Editor

Department of Mechanical Engineering, Brigham Young University, Provo, UT 84602, USA
Interests: spine biomechanics; orthopaedic biomechanics; medical device design; nanocomposite biomaterials

Special Issue Information

Dear Colleagues,

Wearable technologies represent a hugely exciting area of research, promising a sea change in health monitoring, personal fitness, performance, rehabilitation, and safety. At the heart of the wearable revolution are advances in sensor technologies that enable natural interfacing with human activity while capturing detailed biomechanical data. This Special Issue solicits and celebrates advances in sensor technologies that facilitate innovation in the field of wearable applications. Contributions are encouraged in the fields of self-sensing fabrics and foams, flexible strain sensors and arrays, advances in accelerometer and IMU-based systems, and other sensor technologies for tracking human performance or actuating companion technologies based on human movement.

Prof. Dr. David T. Fullwood
Prof. Dr. Anton E. Bowden
Guest Editors

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Published Papers (6 papers)

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Research

Open AccessArticle

Dual-Sensing Piezoresponsive Foam for Dynamic and Static Loading

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Sensors 2023, 23(7), 3719; https://doi.org/10.3390/s23073719 - 04 Apr 2023

Viewed by 614

Abstract

Polymeric foams, embedded with nano-scale conductive particles, have previously been shown to display quasi-piezoelectric (QPE) properties; i.e., they produce a voltage in response to rapid deformation. This behavior has been utilized to sense impact and vibration in foam components, such as in sports padding and vibration-isolating pads. However, a detailed characterization of the sensing behavior has not been undertaken. Furthermore, the potential for sensing quasi-static deformation in the same material has not been explored. This paper provides new insights into these self-sensing foams by characterizing voltage response vs frequency of deformation. The correlation between temperature and voltage response is also quantified. Furthermore, a new sensing functionality is observed, in the form of a piezoresistive response to quasi-static deformation. The piezoresistive characteristics are quantified for both in-plane and through-thickness resistance configurations. The new functionality greatly enhances the potential applications for the foam, for example, as insoles that can characterize ground reaction force and pressure during dynamic and/or quasi-static circumstances, or as seat cushioning that can sense pressure and impact. Full article

(This article belongs to the Special Issue Advances in Sensor Technologies for Wearable Applications)

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Open AccessArticle

Automatic Body Segment and Side Recognition of an Inertial Measurement Unit Sensor during Gait

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Sensors 2023, 23(7), 3587; https://doi.org/10.3390/s23073587 - 29 Mar 2023

Viewed by 608

Abstract

Inertial measurement unit (IMU) sensors are widely used for motion analysis in sports and rehabilitation. The attachment of IMU sensors to predefined body segments and sides (left/right) is complex, time-consuming, and error-prone. Methods for solving the IMU-2-segment (I2S) pairing work properly only for a limited range of gait speeds or require a similar sensor configuration. Our goal was to propose an algorithm that works over a wide range of gait speeds with different sensor configurations while being robust to footwear type and generalizable to pathologic gait patterns. Eight IMU sensors were attached to both feet, shanks, thighs, sacrum, and trunk, and 12 healthy subjects (training dataset) and 22 patients (test dataset) with medial compartment knee osteoarthritis walked at different speeds with/without insole. First, the mean stride time was estimated and IMU signals were scaled. Using a decision tree, the body segment was recognized, followed by the side of the lower limb sensor. The accuracy and precision of the whole algorithm were 99.7% and 99.0%, respectively, for gait speeds ranging from 0.5 to 2.2 m/s. In conclusion, the proposed algorithm was robust to gait speed and footwear type and can be widely used for different sensor configurations. Full article

(This article belongs to the Special Issue Advances in Sensor Technologies for Wearable Applications)

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Open AccessArticle

Adaptive Impedance Matching Network for Contactless Power and Data Transfer in E-Textiles

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Sensors 2023, 23(6), 2943; https://doi.org/10.3390/s23062943 - 08 Mar 2023

Viewed by 802

Abstract

One of the major challenges associated with e-textiles is the connection between flexible fabric-integrated wires and rigid electronics. This work aims to increase the user experience and mechanical reliability of these connections by foregoing conventional galvanic connections in favor of inductively coupled coils. The new design allows for some movement between the electronics and the wires, and it relieves the mechanical strain. Two pairs of coupled coils continuously transmit power and bidirectional data across two air gaps of a few millimeters. A detailed analysis of this double inductive link and associated compensation network is presented, and the sensitivity of the network to changing conditions is explored. A proof of principle is built that demonstrates the system’s ability to self-tune based on the current–voltage phase relation. A demonstration combining 8.5 kbit/s of data transfer with a power output of 62 mW DC is presented, and the hardware is shown to support data rates of up to 240 kbit/s. This is a significant improvement of the performance of previously presented designs. Full article

(This article belongs to the Special Issue Advances in Sensor Technologies for Wearable Applications)

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Open AccessArticle

Upper Limb Position Tracking with a Single Inertial Sensor Using Dead Reckoning Method with Drift Correction Techniques

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Sensors 2023, 23(1), 360; https://doi.org/10.3390/s23010360 - 29 Dec 2022

Viewed by 752

Abstract

Inertial sensors are widely used in human motion monitoring. Orientation and position are the two most widely used measurements for motion monitoring. Tracking with the use of multiple inertial sensors is based on kinematic modelling which achieves a good level of accuracy when biomechanical constraints are applied. More recently, there is growing interest in tracking motion with a single inertial sensor to simplify the measurement system. The dead reckoning method is commonly used for estimating position from inertial sensors. However, significant errors are generated after applying the dead reckoning method because of the presence of sensor offsets and drift. These errors limit the feasibility of monitoring upper limb motion via a single inertial sensing system. In this paper, error correction methods are evaluated to investigate the feasibility of using a single sensor to track the movement of one upper limb segment. These include zero velocity update, wavelet analysis and high-pass filtering. The experiments were carried out using the nine-hole peg test. The results show that zero velocity update is the most effective method to correct the drift from the dead reckoning-based position tracking. If this method is used, then the use of a single inertial sensor to track the movement of a single limb segment is feasible. Full article

(This article belongs to the Special Issue Advances in Sensor Technologies for Wearable Applications)

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Open AccessArticle

Validity of Spatio-Temporal Gait Parameters in Healthy Young Adults Using a Motion-Sensor-Based Gait Analysis System (ORPHE ANALYTICS) during Walking and Running

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Sensors 2023, 23(1), 331; https://doi.org/10.3390/s23010331 - 28 Dec 2022

Cited by 1 | Viewed by 1835

Abstract

Motion sensors are widely used for gait analysis. The validity of commercial gait analysis systems is of great interest because calculating position/angle-level gait parameters potentially produces an error in the integration process of the motion sensor data; moreover, the validity of ORPHE ANALYTICS, a motion-sensor-based gait analysis system, has not yet been examined. We examined the validity of the gait parameters calculated using ORPHE ANALYTICS relative to those calculated using conventional optical motion capture. Nine young adults performed gait tasks on a treadmill at speeds of 2–12 km/h. The three-dimensional position data and acceleration and angular velocity data of the feet were collected. The gait parameters were calculated from motion sensor data using ORPHE ANALYTICS, and optical motion capture data. Intraclass correlation coefficients [ICC_(2,1)] were calculated for relative validities. Eight items, namely, stride duration, stride length, stride frequency, stride speed, vertical height, stance phase duration, swing phase duration, and sagittal angle_IC exhibited excellent relative validities [ICC_(2,1) > 0.9]. In contrast, sagittal angle_TO and frontal angle_IC demonstrated good [ICC_(2,1) = 0.892–0.833] and moderate relative validity [ICC_(2,1) = 0.566–0.627], respectively. ORPHE ANALYTICS was found to exhibit excellent relative validities for most gait parameters. These results suggest its feasibility for gait analysis outside the laboratory setting. Full article

(This article belongs to the Special Issue Advances in Sensor Technologies for Wearable Applications)

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Open AccessArticle

Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable Sensors

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Sensors 2022, 22(23), 9301; https://doi.org/10.3390/s22239301 - 29 Nov 2022

Cited by 1 | Viewed by 1034

Abstract

Mobility impairments are a common symptom of age-related degenerative diseases. Gait features can discriminate those with mobility disorders from healthy individuals, yet phenotyping specific pathologies remains challenging. This study aims to identify if gait parameters derived from two foot-mounted inertial measurement units (IMU) during the 6 min walk test (6MWT) can phenotype mobility impairment from different pathologies (Lumbar spinal stenosis (LSS)—neurogenic diseases, and knee osteoarthritis (KOA)—structural joint disease). Bilateral foot-mounted IMU data during the 6MWT were collected from patients with LSS and KOA and matched healthy controls (N = 30, 10 for each group). Eleven gait parameters representing four domains (pace, rhythm, asymmetry, variability) were derived for each minute of the 6MWT. In the entire 6MWT, gait parameters in all four domains distinguished between controls and both disease groups; however, the disease groups demonstrated no statistical differences, with a trend toward higher stride length variability in the LSS group (p = 0.057). Additional minute-by-minute comparisons identified stride length variability as a statistically significant marker between disease groups during the middle portion of 6WMT (3rd min: p ≤ 0.05; 4th min: p = 0.06). These findings demonstrate that gait variability measures are a potential biomarker to phenotype mobility impairment from different pathologies. Increased gait variability indicates loss of gait rhythmicity, a common feature in neurologic impairment of locomotor control, thus reflecting the underlying mechanism for the gait impairment in LSS. Findings from this work also identify the middle portion of the 6MWT as a potential window to detect subtle gait differences between individuals with different origins of gait impairment. Full article

(This article belongs to the Special Issue Advances in Sensor Technologies for Wearable Applications)

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