Wearable technologies最新文献

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.

Wearable robotic devices (WRD) are still struggling to fulfill their vast potential. Inadequate daily life usability is one of the main hindrances to increased technology acceptance. Improving usability evaluation practices during the development of WRD could help address these limitations. In this work, we present the design and validation of a novel online platform aiming to fill this gap, the Interactive Usability Toolbox (IUT). This platform consists of a public website that offers an interactive, context-specific search within a database of 154 user research methods and educational information about usability. In a dedicated study, the effect of this platform to support usability evaluation was investigated. Twelve WRD experts were asked to complete the task of defining usability evaluation protocols for two specific use cases. The platform was provided to support one of the use cases. The quality and composition of the proposed protocols were assessed by (i) two blinded reviewers, (ii) the participants themselves, and (iii) the study coordinators. We showed that using the IUT significantly affected the proposed evaluation focus, shifting protocols from mainly effectiveness-oriented to more user-focused studies. The protocol quality, as rated by the external reviewers, remained equivalent to those designed with conventional strategies. A mixed-method usability evaluation of the platform yielded an overall positive image, with detailed suggestions for further improvements. The IUT is expected to positively affect the evaluation and development of WRD through its educational value, the context-specific recommendations supporting ongoing benchmarking endeavors, and highlighting the value of qualitative user research.

Cumulative back muscle fatigue plays a role in the occurrence of low-back injuries in occupations that require repetitive lifting of heavy loads and working in forward leaning postures. Lift-support exoskeletons have the potential to reduce back and hip muscle activity, thereby delaying the onset of fatigue in these muscles. Therefore, exoskeletons are being considered a potentially important tool to further reduce workload-related injuries. However, today no standards have been established on how to benchmark the support level of lift-support exoskeletons. This work proposes an experimental protocol to quantify the support level of a lift-support exoskeletons on instant changes in muscle activity and fatigue development while maintaining a static forward leaning posture. It then applies the protocol to experimentally assess the effect of the support provided by a commercially available lift-support exoskeleton, the LiftSuit 2.0 (Auxivo AG, Schwerzenbach, Switzerland), on the user. In a sample of 14 participants, the amplitude of the muscle activity of the back muscles and hip muscles () was significantly reduced. Wearing the exoskeleton significantly reduced the amount of fatigue developed during the task (). Changes in muscle fatigue can be objectively recorded and correlated with relevant changes for exoskeleton users: the time a task can be performed and perceived low-back fatigue. Thus, including such measures of fatigue in standardized benchmarking procedures will help quantify the benefits of exoskeletons for occupational use.

[This corrects the article DOI: 10.1017/wtc.2022.23.].

The development of exoskeletons is currently a lengthy process full of challenges. We are proposing a framework to accelerate the process and make the resulting exoskeletons more user-centered. The needed accomplishments in science are described in an effort to lay the foundation for future research projects. Since the early 2000s, exoskeletons have been discussed as an emerging technology in industrial, medical, or military applications. Those systems are designed to support people during manual tasks. At first, those systems lacked broad acceptance. Many models found their niches in ongoing developments and more diverse systems entering the market. There are still applications that are in dire need of such assistance. Due to the lack of experience with body-worn robotics, the development of such systems has been shaped by trial and error. The lack of legacy products results in longer development times. In this paper, a process to generate a framework is presented to display the required research to enable future exoskeleton designers. Owing to their proximity to the user's body, exoskeletons are highly complex systems that need sophisticated subsystems, such as kinematic, control, interaction design, or actuators, to be accepted by users. Due to the wide variety of fields and high user demands, a synchronized multidisciplinary effort is necessary. To achieve this, a process to develop a modular framework for exoskeleton design is proposed. It focuses on user- and use-case-centered solutions for matching kinematics, actuation, and control. To ensure the usefulness of the framework, an evaluation of the incorporated solutions is required.

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.

Assistive forces transmitted from wearable robots to the robot's users are often defined by controllers that rely on the accurate estimation of the human posture. The compliant nature of the human-robot interface can negatively affect the robot's ability to estimate the posture. In this article, we present a novel algorithm that uses machine learning to correct these errors in posture estimation. For that, we recorded motion capture data and robot performance data from a group of participants (n = 8; 4 females) who walked on a treadmill while wearing a wearable robot, the Myosuit. Participants walked on level ground at various gait speeds and levels of support from the Myosuit. We used optical motion capture data to measure the relative displacement between the person and the Myosuit. We then combined this data with data derived from the robot to train a model, using a grading boosting algorithm (XGBoost), that corrected for the mechanical compliance errors in posture estimation. For the Myosuit controller, we were particularly interested in the angle of the thigh segment. Using our algorithm, the estimated thigh segment's angle RMS error was reduced from 6.3° (2.3°) to 2.5° (1.0°), mean (standard deviation). The average maximum error was reduced from 13.1° (4.9°) to 5.9° (2.1°). These improvements in posture estimation were observed for all of the considered assistance force levels and walking speeds. This suggests that ML-based algorithms provide a promising opportunity to be used in combination with wearable-robot sensors for an accurate user posture estimation.