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Advanced version of DAid® Pressure Sock System (DPSS) for walking/running gait monitoring is proposed. The system enables to study in details time –related parameters of gait, such as foot strike time, stride/swing time, gradients of plantar loading/unloading, etc. both indoor and outdoor. A new method of data processing, analysis and representation is developed for such systems with limited quantity of sensors and implemented in DPSS software. The method considers the foot loading as a propagation of the plantar pressure wave, where separate sensors record amplitudes of pressure waves as reference points. Data processing includes scaling procedure that is based on application of sample entropy as the measure of sensors activity. Processed data are represented in the form of loading wave plots (WPs). WPs show load propagation across the sensors during the gait/stance. Developed system and method were tested for different types of walk/run and corresponding WPs were constructed and evaluated by sport medicine doctors. Good agreement between WPs – based analysis and diagnoses made by direct observation during walk/run tests confirms system efficacy and validity of results. Proposed method essentially simplifies gait studies and interpretation of the results.

Movement retraining has proven to be a reliable way of treating compensatory movement faults, however, it is time-consuming and requires a lot of attention from a physiotherapist. Reliable movement retraining assistance system could enable patients to perform retraining exercises at home, thus increasing the possible exercise performance frequentness and consequently hasten the improvement. This paper describes a smart shirt system for movement retraining assistance. The smart shirt system is based on textile stretch sensors that are positioned over the typical movement fault sites for movement detection and monitoring. The information from the textile stretch sensors is used as visual feedback by the wearer for movement corrections. To verify the feasibility of the retraining assistance system, it was tested with a set of compensatory movement tests and retraining exercises. Each exercise was performed in a correct and incorrect manner, and the sensor measurement was analyzed to confirm the difference in measured signals between both cases. In total 11 exercises were performed to cover all of the typical compensatory movement sites, and for each retraining exercise, at least one sensor was identified, whose measurement could be used as feedback for the particular exercise.

Periodic modulation of the deposition angle (PMDA) is a new method to deposit nanostructured and continuous layers with controllable periodic density fluctuation. The method is used for the magnetron sputtering of a WO3layer for an electrochromic device (ECD). An experimental study indicates that the electrochromic coloration-bleaching rate nearly doubles and the electrochromic efficiency grows by about 25% in comparison with the traditional method. The ECD efficiency rises with the increasing degree of nanostructure ordering, surface roughness, and homogeneity of the WO3layer. The method is promising for coating deposition techniques needed to produce versatile devices with specific requirements for ion transport in surface layers, coatings, and interfaces, such as fuel cells, batteries, and supercapacitors.

Manual therapy is used to treat patients with neck pain, and mobilization techniques are practiced clinically by physiotherapists. To evaluate the effectiveness of therapy it is necessary to measure the applied manual therapy forces. We present the sensitive glove concept designed to measure the forces applied during therapy. The sensitive glove is equipped with multiple textile-based sensors connected by sewing technology, in which changes in electrical resistance were observed due to the applied force. The number and position of sensors were defined basing on the experience of manual therapists and practicable techniques. Glove fabrication technologies were tested practically – sensors were glued and sewn on the base material and simple conductive threads were sewn by hand and with a sewing machine. Technological solutions were evaluated both in terms of visual and technical aspects. The final version of the sensitive glove was assessed by an expert and recommendations were given for improving further prototypes.

Present paper relates to the field of “smart textiles” and demonstrates the results of research carried out on the development of a new type of knitted sensing elements (knitted resistive fabric (KRP)) which can be integrated in the garment applied for health monitoring. KRP produced from conductive resistive, nonconductive isolating and nonconductive elastomeric yarns. Elastomeric yarn is used as the base yarn and is knitted in the whole fabric, but isolating and resistive yarns (functional yarns) are used as face yarns and knitted in specific sequence. Proposed knitted resistive fabric has high sensitivity to strain deformation and can be used as sensing element to control deformation of the human body parts, joint motion, respiration etc.

Feet plantar pressure measurement and gait pattern analysis can be utilized for different disease detection as well as post-operation rehabilitation monitoring. Increasingly popular for gait analysis in research have become smart sock systems as an alternative to the more expensive commercial analogs, however, analysis with such systems are complicated due to the limited sensor count and other inherit limitations of textile pressure sensors. This paper proposes a new method for gait pattern recognition for use with smart sock systems. According to this method, steps are classified depending on the sum of Manhattan distances (SMD) between two high-dimension vectors, from which one describes the step to analyze and the other is a predefined vector representing a specific gait pattern. The gait pattern with the lowest SMD value to the particular step vector is assigned to each step. An experiment was performed to verify the proposed method, where three volunteers simulated three gait types, normal, pronation and supination, and the simulated steps were compared to references. The experimental results confirmed that typically the lowest SMD value was obtained by the reference corresponding to the tested gait pattern type. In one of the demonstrated cases, the mean SMD value for the same set of steps with the reference for normal step was 1.26, 1.77, and 1.78, with reference for pronation it was 1.75, 1.57, and 2.59, and reference for supination, 1.75, 2.15 and 1.46, for normal gait, gait with pronation and gait with supination, respectively. It can be seen that the lowest mean SMD value for each gait type is when the correct reference is applied.

The aim of the study was to present a new DAid(r)Pressure Sock System for feet locomotion monitoring and to verify it's temporal characteristics by data comparison with the same obtained by two other widely used methods as reference. Designed system is based on sensors which can be knitted directly in the garment or hosiery items. DAid(r)Pressure Sock System was created for sport and medical applications. Comparison of temporal characteristics of different types of locomotion, obtained using designed system and reference devises, showed good agreement between data.