Gait analysis is a fundamental tool in biomechanics and rehabilitation, as it evaluates human movements’ kinematic and kinetic behavior. For this reason, high-precision devices have been developed. However, these require controlled environments, which generates a deficiency in the capacity of studies related to gait analysis in outdoor and indoor scenarios. Therefore, this article describes the development and testing of a wearable system to measure gait cycle kinematic and kinetic parameters. The methodology for the development of the system includes the assembly of modules with commercial surface electromyography (sEMG) sensors and inertial measurement sensors, as well as the use of instrumented insoles with force-resistive sensors, and the design of the software to acquire, process, visualize, and store the data. The system design considers portability, rechargeable battery power supply, wireless communication, acquisition speed suitable for kinematic and kinetic signals, and compact size. Also, it allows simultaneous assessment of sEMG activity, hip and knee joint angles, and plantar pressure distribution, using a wireless connection via Wi-Fi and user datagram protocol for data transmission with a synchronization accuracy of 576 μs, data loss of 0.8%, and autonomy of 167 min of continuous operation, enabling uninterrupted data acquisition for gait analysis. To demonstrate its performance, the system was tested on 10 subjects without any neuromusculoskeletal pathology in indoor and outdoor environments, evaluating relevant parameters that facilitate a comprehensive analysis of gait in various contexts. The system offers a reliable, versatile, and affordable alternative for gait assessment in outdoor and indoor environments.

The manufacturing industry, notably the aeronautics sector, involves tasks presenting risks of low back pain. One of the preventive strategies could be the use of passive back exoskeletons, which have demonstrated benefits during activities involving trunk bending. This study aims to evaluate the effects of four passive back exoskeletons on trunk neuromuscular activity, kinematics, and perceived discomfort during polishing tasks simulated in a laboratory setting. Nineteen participants performed four tasks (two static bending tasks and two load-carrying tasks) without and with two soft (CORFOR and BionicBack) and two rigid (BackX and Laevo FLEX) exoskeletons. The results showed varying effects depending on the tested exoskeleton model, beyond the distinction between rigid and soft designs. Reductions in lumbar erector spinae (LES) neuromuscular activity were observed with Laevo FLEX and CORFOR during static tasks compared to the condition without exoskeleton (8–18%; p < .05). However, reductions in LES muscle activity were not significant during load carrying. Biceps femoris neuromuscular activity was significantly lower in the four tasks when using the Laevo FLEX, with reductions ranging from 8 to 17% (p < .01). The two rigid exoskeletons decreased perceived back discomfort across all tasks (p < .05). Finally, the BionicBack exoskeleton significantly altered participants’ kinematics across all four tasks, reducing both trunk range of motion and average flexion (p < .05). The Laevo FLEX exoskeleton was the only one to significantly reduce both neuromuscular activity and perceived back discomfort, while causing no adverse effects, appearing advantageous when polishing in the aeronautical industry.

Real-time measurement of head rotation, a primary human body movement, offers potential advantages in rehabilitating head or neck motor disorders, promoting seamless human–robot interaction, and tracking the lateral glance of children with autism spectrum disorder for effective intervention. However, existing options such as cameras capturing the entire face or skin-attached sensors have limitations concerning privacy, safety, and/or usability. This research introduces a novel method that employs a battery-free RFID tag-based wearable sensor for monitoring head orientation, as a substitute for the existing options like camera. By attaching a pair of passive RFID tags to the front of the head at a specific distance from each other, the signal strength of each tag within the pair differs based on the discrepancy in distance from the RFID reader caused by head rotation. Important parameters including distance between the tags, distance from the reader, and tag types, are investigated to suggest optimal sensor design. In tests involving random head rotations by 10 healthy adults, there was a significant correlation between the orientation of the head and gaze in the yaw direction and the differences in signal strength from the sensor pairs. The correlation coefficients ($ {r}^2 $) were satisfactory, at 0.88 for head and 0.83 for left eye pupil orientations. However, the sensor failed to estimate pitch rotations for head and gaze, due to the insufficient vertical spacing between the tags. No demographic factors appeared to influence the results.

Internal and external rotation of the shoulder is often challenging to quantify in the clinic. Existing technologies, such as motion capture, can be expensive or require significant time to setup, collect data, and process and analyze the data. Other methods may rely on surveys or analog tools, which are subject to interpretation. The current study evaluates a novel, engineered, wearable sensor system for improved internal and external shoulder rotation monitoring, and applies it in healthy individuals. Using the design principles of the Japanese art of kirigami (folding and cutting of paper to design 3D shapes), the sensor platform conforms to the shape of the shoulder with four on-board strain gauges to measure movement. Our objective was to examine how well this kirigami-inspired shoulder patch could identify differences in shoulder kinematics between internal and external rotation as individuals moved their humerus through movement patterns defined by Codman’s paradox. Seventeen participants donned the sensor while the strain gauges measured skin deformation patterns during the participants’ movement. One-dimensional statistical parametric mapping explored differences in strain voltage between the rotations. The sensor detected distinct differences between the internal and external shoulder rotation movements. Three of the four strain gauges detected significant temporal differences between internal and external rotation (all p < .047), particularly for the strain gauges placed distal or posterior to the acromion. These results are clinically significant, as they suggest a new class of wearable sensors conforming to the shoulder can measure differences in skin surface deformation corresponding to the underlying humerus rotation.

Linkage fabrics are gaining in popularity and finding applications in architecture, aerospace, healthcare, and fashion because they can deliver materials with bespoke flexibility and strength through the geometric design of linkage nodes. In this article, we provide a perspective on linkage fabrics as a new class of programmable materials. We describe the theory and design principles of these linkage fabrics and show how they can be designed and simulated using digital tools, and fabricated using 3D printing. This digital approach overcomes a major obstacle to the adoption of these materials, namely their complexity. We show how simulation methods can be verified and calibrated through experimental testing. This perspective article also discusses design-led research challenges for linkage fabrics such as the development of wearable assistive devices for those with physical disabilities.

Flexible electronics researchers have been conducting studies to explore the response of flexible stretchable electrodes to strain. The regulation of strain response in current flexible stretchable electrodes relies primarily on altering the material system, interfacial adhesion, or electrode structure. However, modifying the material system or interfacial adhesion can negatively disrupt the stretchable electrode preparation process, making commercialization a significant challenge. Additionally, the material system may be inadequate in extreme environments such as high temperatures. Hence a systematic structural design approach is crucial for effective response modulation of stretchable electrodes. One potential solution is the design of fibre structures from the micro to macro scale. This article focuses on discussing how the response of stretchable electrodes can be modulated by fibres in different states. The discussion includes fibres on elastic films, fibres directly constituting fibrous membranes at the microscopic level, and fibres constituting metamaterials at the fine level. The modulation can be achieved by altering the orientation of the fibres, the geometrical structure of the fibres themselves, and the geometrical structure formed between the fibres. Additionally, the article analyses the current situation of stretchable electrodes in extreme environments such as high temperatures. It also reviews the development of ceramic fibre membranes that can be stretched in high-temperature environments. The authors further discuss how the stretchability of ceramic fibre membranes can be improved through the structuring of ceramic fibre membranes with metamaterials. Ultimately, the goal is to realize stretchable electrodes that can be used in extreme environments such as high temperatures.

Current laboratory-based setups (optical marker cameras + force plates) for human motion measurement require participants to stay in a constrained capture region which forbids rich movement types. This study established a fully wearable system, based on commercially available sensors (inertial measurement units + pressure insoles), that can measure both kinematic and kinetic motion data simultaneously and support wireless frame-by-frame streaming. In addition, its capability and accuracy were tested against a conventional laboratory-based setup. An experiment was conducted, with 9 participants wearing the wearable measurement system and performing 13 daily motion activities, from slow walking to fast running, together with vertical jump, squat, lunge, and single-leg landing, inside the capture space of the laboratory-based motion capture system. The recorded sensor data were post-processed to obtain joint angles, ground reaction forces (GRFs), and joint torques (via multi-body inverse dynamics). Compared to the laboratory-based system, the established wearable measurement system can measure accurate information of all lower limb joint angles (Pearson’s r = 0.929), vertical GRFs (Pearson’s r = 0.954), and ankle joint torques (Pearson’s r = 0.917). Center of pressure (CoP) in the anterior–posterior direction and knee joint torques were fairly matched (Pearson’s r = 0.683 and 0.612, respectively). Calculated hip joint torques and measured medial–lateral CoP did not match with the laboratory-based system (Pearson’s r = 0.21 and 0.47, respectively). Furthermore, both raw and processed datasets are openly accessible (https://doi.org/10.5281/zenodo.6457662). Documentation, data processing codes, and guidelines to establish the real-time wearable kinetic measurement system are also shared (https://github.com/HuaweiWang/WearableMeasurementSystem).

This study presents a new wearable insole pressure sensor (IPS), composed of fabric coated in a carbon nanotube-based composite thin film, and validates its use for quantifying ground reaction forces (GRFs) during human walking. Healthy young adults (n = 7) walked on a treadmill at three different speeds while data were recorded simultaneously from the IPS and a force plate (FP). The IPS was compared against the FP by evaluating differences between the two instruments under two different assessments: (1) comparing the two peak forces at weight acceptance and push-off (2PK) and (2) comparing the absolute maximum (MAX) of each gait cycle. Agreement between the two systems was evaluated using the Bland–Altman method. For the 2PK assessment, the group mean of differences (MoD) was −1.3 ± 4.3% body weight (BW) and the distance between the MoD and the limits of agreement (2S) was 25.4 ± 11.1% BW. For the MAX assessment, the average MoD across subjects was 1.9 ± 3.0% BW, and 2S was 15.8 ± 9.3% BW. The results of this study show that this sensor technology can be used to obtain accurate measurements of peak walking forces with a basic calibration and consequently open new opportunities to monitor GRF outside of the laboratory.

The development of wearable technology, which enables motion tracking analysis for human movement outside the laboratory, can improve awareness of personal health and performance. This study used a wearable smart sock prototype to track foot–ankle kinematics during gait movement. Multivariable linear regression and two deep learning models, including long short-term memory (LSTM) and convolutional neural networks, were trained to estimate the joint angles in sagittal and frontal planes measured by an optical motion capture system. Participant-specific models were established for ten healthy subjects walking on a treadmill. The prototype was tested at various walking speeds to assess its ability to track movements for multiple speeds and generalize models for estimating joint angles in sagittal and frontal planes. LSTM outperformed other models with lower mean absolute error (MAE), lower root mean squared error, and higher R-squared values. The average MAE score was less than 1.138° and 0.939° in sagittal and frontal planes, respectively, when training models for each speed and 2.15° and 1.14° when trained and evaluated for all speeds. These results indicate wearable smart socks to generalize foot–ankle kinematics over various walking speeds with relatively low error and could consequently be used to measure gait parameters without the need for a lab-constricted motion capture system.

Rapid analysis of animal welfare is a crucial component of the assessment of the meat quality supply chain, ensuring management procedures confer optimum standards of welfare. Further, there is increasing interest in monitoring the welfare state of each individual animal. This study looked at transport and pre-slaughter management in terms of meat quality evaluated in two breeds (Biellese and Sambucana) across two different farming systems. Precision Livestock Farming (PLF) technologies were implemented, including accelerometer and rumination activity ear-tag sensors, as potential welfare indicators during transportation and pre-slaughter. Significant correlations were found between sensors’ parameters, such as total activity and rumination and physical and chemical meat quality characteristics such as drip loss. Lambs with lower rumination and/or lower total activity were found to have lower drip loss indicating reduced meat quality. Sensors have the potential to help detect those animals particularly sensitive to stressors during transport and pre-slaughter handling and may allow real-time measurement of the impact of transport and handling in abattoirs, enabling better animal management via specific customised strategies.

Though early intervention can improve outcomes for children with motor disabilities, delays in diagnosis can impact the success of intervention programs. Prior work indicates that spontaneous kicking patterns can be used to model typical infant motor development to assist in the early detection of motor delays. However, abnormalities in spontaneous movements are not well defined or readily observable through traditional functional assessments. In this research, a method is introduced for the early detection of delays through the assessment of spontaneous kicking data gathered using a wearable sensing suit. We present formulations of kinematic features identified in the clinical space, identify which features are significant predictors of infant age, and establish normative values. Finally, we offer an analysis of preterm (PT) infant data compared to normative values derived from term infants. Term and PT infants ranging in age from 1 to 10 months were studied. We found that frequency, duration, acceleration, inter-joint coordination, and maximum joint excursion metrics had a significant correlation with age. From these features, models of typical kicking development were created using data from term, typically developing infants. When compared to normative trends, PT infants display differing developmental trends.

Multimodal digital data registered with wearable biosensors have emerged as highly complementary of clinical pencil-and-paper criteria, offering new insights in ways to detect and diagnose various aspects of Parkinson’s disease (PD). A pressing question is how to combine both the clinical knowledge of PD and the new technology to create interpretable digital biomarkers easily obtainable with off-the-shelf technology. Several challenges concerning disparity in biophysical units, anatomical differences across participants, sensor positioning, and sampling resolution are addressed in this work, along with identification of optimal parameters to automatically differentiate patients with PD from controls. We combine data from a multitude of biosensors registering signals from the central (electroencephalography) and peripheral (magnetometry, kinematics) nervous systems, inclusive of the autonomic nervous system (electrocardiogram), as the participants perform natural tasks requiring different levels of intentional planning and automatic control. We find that magnetometer data during walking, across a variety of amplitude and timing signals, provide optimal separation of PD from neurotypical controls. We conclude that using multimodal signals within the context of actions that bear different levels of intent, can be revealing of features of PD that would scape the naked eye. Further, we add that clinical criteria combined with such optimal digital parameter spaces offer a far more complete picture of PD than using either one of these pieces of data alone.

In electronic musical instruments (EMIs), the concept of “sound sculpting” was proposed by Mulder, in which imaginary objects are manually sculpted to produce sounds, although promising has had some limitations: driven by pure intuition, only the objects’ geometrical properties were mapped to sound, while effort—which is often regarded as a key factor of expressivity in music performance—was neglected. The aim of this paper is to enhance such digital interactions by accounting for the perceptual measure of effort that is conveyed through well-established gesture-sound links in the ecologically valid conditions of non-digital music performances. Thus, it reports on the systematic exploration of effort in Dhrupad vocal improvisation, in which singers are often observed to engage with melodic ideas by manipulating intangible, imaginary objects with their hands. The focus is devising formalized descriptions to infer the amount of effort that such interactions are perceived to require and classify gestures as interactions with elastic versus rigid objects, based on original multimodal data collected in India for the specific study. Results suggest that a good part of variance for both effort levels and gesture classes can be explained through a small set of statistically significant acoustic and movement features extracted from the raw data and lead to rejecting the null hypothesis that effort is unrelated to the musical context. This may have implications on how EMIs could benefit from effort as an intermediate mapping layer and naturally opens discussions on whether physiological data may offer a more intuitive measure of effort in wearable technologies.

Problem solving, and specifically the goal of developing problem-solving competences, is a significant dimension of environmental education. Moreover, human senses and electronic sensors have been recognized as important tools in authentic problem-based learning. The purpose of this paper is to present a model to support teachers in creating didactic activities that use human senses and electronic sensors as epistemic mediators in participatory environmental education problem-based learning. The EcoSolvingS model is based on a set theoretical and practical perspectives, and on a cross analysis of a selection of environmental education problem-solving case studies. In a first part, this paper presents the dimensions of the theoretical foundations of the EcoSolvingS model. Subsequently, the results of the cross analysis of the environmental education problem-solving case studies are presented and related to the components of the EcoSolvingS model. Finally, the model is described, and its utility and future developments are discussed.