Measuring Surface Electromyography with Textile Electrodes in a Smart Leg Sleeve

This paper presents the design, development, and validation of a novel e-textile leg sleeve for non-invasive Surface Electromyography (sEMG) monitoring. This wearable device incorporates e-textile sensors for sEMG signal acquisition from the lower limb muscles, specifically the anterior tibialis and lateral gastrocnemius. Validation was conducted by performing a comparative study with eleven healthy volunteers to evaluate the performance of the e-textile sleeve in acquiring sEMG signals compared to traditional Ag/AgCl electrodes. The results demonstrated strong agreement between the e-textile and conventional methods in measuring descriptive metrics of the signals, including area, power, mean, and root mean square. The paired data t-test did not reveal any statistically significant differences, and the Bland-Altman analysis indicated negligible bias between the measures recorded using the two methods. In addition, this study evaluated the wearability and comfort of the e-textile sleeve using the Comfort Rating Scale (CRS). Overall, the scores confirmed that the proposed device is highly wearable and comfortable, highlighting its suitability for everyday use in patient care.

Biomechanical Effects of Using a Passive Exoskeleton for the Upper Limb in Industrial Manufacturing Activities: A Pilot Study

This study investigates the biomechanical impact of a passive Arm-Support Exoskeleton (ASE) on workers in wool textile processing. Eight workers, equipped with surface electrodes for electromyography (EMG) recording, performed three industrial tasks, with and without the exoskeleton. All tasks were performed in an upright stance involving repetitive upper limbs actions and overhead work, each presenting different physical demands in terms of cycle duration, load handling and percentage of cycle time with shoulder flexion over 80°. The use of ASE consistently lowered muscle activity in the anterior and medial deltoid compared to the free condition (reduction in signal Root Mean Square (RMS) -21.6% and -13.6%, respectively), while no difference was found for the Erector Spinae Longissimus (ESL) muscle. All workers reported complete satisfaction with the ASE effectiveness as rated on Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST), and 62% of the subjects rated the usability score as very high (>80 System Usability Scale (SUS)). The reduction in shoulder flexor muscle activity during the performance of industrial tasks is not correlated to the level of ergonomic risk involved. This preliminary study affirms the potential adoption of ASE as support for repetitive activities in wool textile processing, emphasizing its efficacy in reducing shoulder muscle activity. Positive worker acceptance and intention to use ASE supports its broader adoption as a preventive tool in the occupational sector.

Effects of Ankle-Foot Orthosis on Balance of Foot Drop Patients

Ankle-Foot Orthoses (AFOs) are common non-surgical treatments used to support foot and ankle joint when their normal functioning is compromised. AFOs have relevant impact on gait biomechanics, while scientific literature about effects on static balance is less strong and confusing. This study aims to assess the effectiveness of a plastic semi-rigid AFO in improving static balance on foot drop patients. Results underline that no significant effects on static balance is obtained on the study population when the AFO is used on the impaired foot.

Reliability of IMU-Derived Gait Parameters in Foot Drop Patients

Foot drop is a deficit in foot dorsiflexion causing difficulties in walking. Passive ankle-foot orthoses are external devices used to support the drop foot improving gait functions. Foot drop deficits and therapeutic effects of AFO can be highlighted using gait analysis. This study reports values of the major spatiotemporal gait parameters assessed using wearable inertial sensors on a group of 25 subjects suffering from unilateral foot drop. Collected data were used to assess the test-retest reliability by means of Intraclass Correlation Coefficient and Minimum Detectable Change. Excellent test-retest reliability was found for all the parameters in all walking conditions. The analysis of Minimum Detectable Change identified the gait phases duration and the cadence as the most appropriate parameters to detect changes or improvements in subject gait after rehabilitation or specific treatment.

The E-Textile for Biomedical Applications: A Systematic Review of Literature

The use of e-textile technologies spread out in the scientific research with several applications in both medical and nonmedical world. In particular, wearable technologies and miniature electronics devices were implemented and tested for medical research purposes. In this paper, a systematic review regarding the use of e-textile for clinical applications was conducted: the Scopus and Pubmed databases were investigate by considering research studies from 2010 to 2020. Overall, 262 papers were found, and 71 of them were included in the systematic review. Of the included studies, 63.4% focused on information and communication technology studies, while the other 36.6% focused on industrial bioengineering applications. Overall, 56.3% of the research was published as an article, while the remainder were conference papers. Papers included in the review were grouped by main aim into cardiological, muscular, physical medicine and orthopaedic, respiratory, and miscellaneous applications. The systematic review showed that there are several types of applications regarding e-textile in medicine and several devices were implemented as well; nevertheless, there is still a lack of validation studies on larger cohorts of subjects since the majority of the research only focuses on developing and testing the new device without considering a further extended validation.

A multiple linear regression approach to extimate lifted load from features extracted from inertial data

Work-related musculoskeletal disorders are among the main occupational health problems. Substantial evidence has shown that work-related physical risk factors are the main source of low back complaints, particularly affecting heavy and repetitive manual lifting activities. The aim of the study is, during load lifting tasks, to explore the correlation between the time domain features extracted from the acceleration and angular velocity signals of the performing subject and the load lifted, and to explore the feasibility of a multiple linear regression model to predict the lifted load. The acceleration and angular velocity signals were acquired along the three directions of space by means of an inertial sensor placed on the subject's chest, during lifting activities with load gradually increased by 1 kg from 0 kg to 18 kg. Successively three time-domain features (Root Mean Square, Standard Deviation and MinMax value) were extracted from the acquired signals. First a correlation analysis was carried out between each individual feature and the load lifted (calculating r); then the time-domain features that proved most representative (strong correlation) were used to create a multiple linear regression model (calculating R-square). The statistical analysis was carried out by means of the Pearson correlation and multiple linear regression model was fed with the most informative time-domain features according to the correlation analysis. The correlation analysis showed a strong correlation (r > 0,7) between six features (three extracted from z-axes acceleration and three extracted from y-axes angular velocity) and the lifted load. The predictive multiple linear regression model, fed with these six features achieved a Rsquare greater than 0,9.The study demonstrated that the proposed combination of kinematic features and a multiple regression model represents a valid approach to automatically calculate the load lifted based on raw signals obtained by means of an inertial sensor placed on the chest. The results confirm the potential application of this methodology to indirectly monitor the load lifted by workers during their activity.

Design and Validation of an E-Textile-Based Wearable Sock for Remote Gait and Postural Assessment

This paper presents a new wearable e-textile based system, named SWEET Sock, for biomedical signals remote monitoring. The system includes a textile sensing sock, an electronic unit for data transmission, a custom-made Android application for real-time signal visualization, and a software desktop for advanced digital signal processing. The device allows the acquisition of angular velocities of the lower limbs and plantar pressure signals, which are postprocessed to have a complete and schematic overview of patient's clinical status, regarding gait and postural assessment. In this work, device performances are validated by evaluating the agreement between the prototype and an optoelectronic system for gait analysis on a set of free walk acquisitions. Results show good agreement between the systems in the assessment of gait cycle time and cadence, while the presence of systematic and proportional errors are pointed out for swing and stance time parameters. Worse results were obtained in the comparison of spatial metrics. The "wearability" of the system and its comfortable use make it suitable to be used in domestic environment for the continuous remote health monitoring of de-hospitalized patients but also in the ergonomic assessment of health workers, thanks to its low invasiveness.