Remote Biofeedback Method for Biomedical Data Analysis

In recent years, the introduction of methods supported by technology has positively modified the traditional paradigm of rehabilitation. Interactive systems have been developed to facilitate patient involvement and to help therapist in patient\u2019s management. ReMoVES (REmote MOnitoring Validation Engineering System) platform addresses the problem of continuity of care in a smart and economical way. It can help patients with neurological, post-stroke and orthopedic impairments in recovering physical, psychological and social functions; such system will not only improve the quality of life and accelerate the recovery process for patients, but also aims at rationalizing and help the manpower required monitoring and coaching individual patients at rehabilitation centers. In order to help and support therapist work, the Remote Biofeedback Method is proposed as an instrument to understand how the patient has executed the rehabilitation exercises without seeing him directly. Therefore, the purpose of this method is to demonstrate that through the joint observation of data from simple sensors, it is possible to determine: time and method of execution of the exercises, performance and improvements during the rehabilitation session, pertinence of exercise and plan of care. The system, during the rehabilitation session, automatically transmits patient\u2019s biofeedback through three different channels: movement, physiological signals and a questionnaire. The therapist uses patient\u2019s data to determine whether the plan of care assigned is appropriate for the recovery of lost functionalities. He will then return a remote feedback to the patient who will not see any kind of graphical or verbal output, but you will see lighter rehabilitative session if it was too difficult or more intense if one assigned was too simple. The rehabilitation protocol proposed consists of the performance of different exercises, which begins with a breathing activity, designed to relax the patient before the \u201ceffective\u201d rehabilitation session. To make the subject comfortable, and to bring again the heartbeat to a basal value, before the rehabilitation session, the patient, in a sitting position, is leading to breathing with a regular rhythm by following a \u201cbreath ball\u201d. From the results obtained in the breathing exercise, it can be concluded that the negative trend of the regression line that approximates the heartbeat signal is an index of relaxation, principal goal for which the exercise was designed. The proposed activities include execution of reaching and grasping, balance and control posture functional exercises, masked through serious games to simulate some of the most common gestures of daily life. In some exercises, a cognitive component will also be involved in achieving the goal required by the activity. For each activity, heart rate, gameplay scores, and different motion parameters were captured and analyzed depending on the type of exercise performed. The heart rate was used as an indicator of motivation and involvement during the execution of several rehabilitative exercises. Others parameters analyzed are the score obtained during the execution of the task, and the time interval between the execution of one exercise and the following one. In addition to the analysis of the individual signals, a preliminary analysis of the correlation between the trend of the heart rate and the performance of the score was also carried out. The results showed that heartbeat in conjunction with score and inter-exercise time could be a high-quality indicator of a patient\u2019s status. The indicators extracted, in fact, in most cases, correspond to the information reported from the therapist who observed the patients during the rehabilitation session. A deep analysis of movement signal was carried on, with the extraction of several indicators for the different body segments involved in rehabilitation, such as the upper limb, the hand, the lower limbs and the posture, included the detection of compensation strategies to reach the targets proposed by the exercise. The results have been extracted by comparing the patient performance to a model extracted by a healthy subjects group. Of particular importance is the spatial map for patients with neglect, an innovative tool that traces the positions where the movement was performed and also provides the therapist with the spatial coordinates where the targets were proposed. Another innovative aspect is the analysis of Center of Pressure (CoP) without the use of a specific footboard, but only through the processing of data from the motion sensor. The results obtained by the application of the Remote Biofeedback Methods to the signals acquired during ReMoVES testing phase show interesting applications of the method to the clinical practice. In fact, the indicators extracted show a realistic correspondence between the disabilities affected the patients and the performance obtained during the execution of the exercises. From the study of the different exercises it can be concluded that the analysis of the signals and the parameters extracted individually, do not provide enough information to outline how the rehabilitation exercise has been executed. By combining the different indicators, it is possible to outline an accurate picture that allows the therapist to make decisions about the assigned plan of care. In conclusion, the Remote Biofeedback Method proposed is now ready to be tested on a wider dataset in order to be consolidated on a larger number of athologies and to associate, if necessary, particular indicators to a particular disease. The future steps will be, a creation of a model starting from patients signals, in order to have a better comparison term, and a testing phase on a larger number of patients, following a clinical protocol, subdividing subject by disease

Surface Functionalization of Fabrics and Threads for Smart Textiles

The future of electronics is moving toward wearable devices and therefore requires a shift away from hard, inflexible materials towards fibers, threads, and fabrics that conform to the shape of the body. Therefore new methods for incorporating textiles as electronic components are needed to replace conventional processing techniques used with smooth, flat substrates like glass, silicon, and many polymers. Toward this end, this work investigates different methods that can be used to tune textile surfaces for electronic functionality, including weaving, solution grafting, and initiated chemical vapor deposition (iCVD). While all of these methods were used to make triboelectrically-active textiles, iCVD combined with simple solution chemistry was also used to synthesize ionically conductive thin films on textiles for solid-state electrolytes. In general these methods elucidate facile pathways towards smart clothing fabrication

Low-power Wearable Healthcare Sensors

Advances in technology have produced a range of on-body sensors and smartwatches that can be used to monitor a wearer’s health with the objective to keep the user healthy. However, the real potential of such devices not only lies in monitoring but also in interactive communication with expert-system-based cloud services to offer personalized and real-time healthcare advice that will enable the user to manage their health and, over time, to reduce expensive hospital admissions. To meet this goal, the research challenges for the next generation of wearable healthcare devices include the need to offer a wide range of sensing, computing, communication, and human–computer interaction methods, all within a tiny device with limited resources and electrical power. This Special Issue presents a collection of six papers on a wide range of research developments that highlight the specific challenges in creating the next generation of low-power wearable healthcare sensors

Effect of flow pattern at pipe bends on corrosion behaviour of low carbon steek and its challenges

Recent design work regarding seawater flow lines has emphasized the need to identify, develop, and verify critical relationships between corrosion prediction and flow regime mechanisms at pipe bend. In practice this often reduces to an pragmatic interpretation of the effects of corrosion mechanisms at pipe bends. Most importantly the identification of positions or sites, within the internal surface contact areas where the maximum corrosion stimulus may be expected to occur, thereby allowing better understanding, mitigation, monitoring and corrosion control over the life cycle. Some case histories have been reviewed in this context, and the interaction between corrosion mechanisms and flow patterns closely determined, and in some cases correlated. Since the actual relationships are complex, it was determined that a risk based decision making process using selected ‘what’ if corrosion analyses linked to ‘what if’ flow assurance analyses was the best way forward. Using this in methodology, and pertinent field data exchange, it is postulated that significant improvements in corrosion prediction can be made. This paper outlines the approach used and shows how related corrosion modelling software data such as that available from corrosion models Norsok M5006, and Cassandra to parallel computational flow modelling in a targeted manner can generate very noteworthy results, and considerably more viable trends for corrosion control guidance. It is postulated that the normally associated lack of agreement between corrosion modelling and field experience, is more likely due to inadequate consideration of corrosion stimulating flow regime data, rather than limitations of the corrosion modelling. Applications of flow visualization studies as well as computations with the k-ε model of turbulence have identified flow features and regions where metal loss is a maximu

Distributed Fiber Ultrasonic Sensor and Pattern Recognition Analytics

Ultrasound interrogation and structural health monitoring technologies have found a wide array of applications in the health care, aerospace, automobile, and energy sectors. To achieve high spatial resolution, large array electrical transducers have been used in these applications to harness sufficient data for both monitoring and diagnoses. Electronic-based sensors have been the standard technology for ultrasonic detection, which are often expensive and cumbersome for use in large scale deployments. Fiber optical sensors have advantageous characteristics of smaller cross-sectional area, humidity-resistance, immunity to electromagnetic interference, as well as compatibility with telemetry and telecommunications applications, which make them attractive alternatives for use as ultrasonic sensors. A unique trait of fiber sensors is its ability to perform distributed acoustic measurements to achieve high spatial resolution detection using a single fiber. Using ultrafast laser direct-writing techniques, nano-reflectors can be induced inside fiber cores to drastically improve the signal-to-noise ratio of distributed fiber sensors. This dissertation explores the applications of laser-fabricated nano-reflectors in optical fiber cores for both multi-point intrinsic Fabry–Perot (FP) interferometer sensors and a distributed phase-sensitive optical time-domain reflectometry (φ-OTDR) to be used in ultrasound detection. Multi-point intrinsic FP interferometer was based on swept-frequency interferometry with optoelectronic phase-locked loop that interrogated cascaded FP cavities to obtain ultrasound patterns. The ultrasound was demodulated through reassigned short time Fourier transform incorporating with maximum-energy ridges tracking. With tens of centimeters cavity length, this approach achieved 20kHz ultrasound detection that was finesse-insensitive, noise-free, high-sensitivity and multiplex-scalability. The use of φ-OTDR with enhanced Rayleigh backscattering compensated the deficiencies of low inherent signal-to-noise ratio (SNR). The dynamic strain between two adjacent nano-reflectors was extracted by using 3×3 coupler demodulation within Michelson interferometer. With an improvement of over 35 dB SNR, this was adequate for the recognition of the subtle differences in signals, such as footstep of human locomotion and abnormal acoustic echoes from pipeline corrosion. With the help of artificial intelligence in pattern recognition, high accuracy of events’ identification can be achieved in perimeter security and structural health monitoring, with further potential that can be harnessed using unsurprised learning

Tribocorrosion of hard-on-hard total hip replacements with metal and ceramic counterfaces under standard and adverse loading conditions

28 mm Metal-on-Metal (MoM) and Metal-on-Ceramic (MoC) Total Hip Replacements were articulated to 1 million cycles under both Standard Gait and Microseparation conditions. The hip simulator was fully instrumented with a three-electrode electrochemical cell to facilitate monitoring of corrosive degradation. The estimated volume loss from corrosion at the bearing surface was seen to increase by nearly an order of magnitude for both devices, representing as much as 17% of total degradation. Anodic current transients also displayed near order of magnitude increases in the peak current for both bearing couples. An adverse loading scenario could cause as much as an order of magnitude increase in the metallic ions released into the joint capsule as well as an increased volume of wear debris

The effect of electrically induced strength training on triceps brachii strength

Twenty-seven apparently healthy females volunteered for the study to compare the effects of neuromuscular electrical stimulation (NMES) (N = 7), traditional isotonic weight training (N = 9) and NMES combined with isotonic weight training (N = 11) on triceps branchii strength. Strength was measured isotonically (1-RM) and isometrically (MVIC). The Weight and Weight/NMES groups performed 3 sets of 8 to 10 reps at 60% of 1-RM, 3 times per week. The NMES group received NMES 3 times per week. All groups were pre- and post-tested to determine 1-RM and Mvic Strength was also tested after four weeks, in order to ensure the Weight and NMES/Weight groups were training at a minimum of 60% of 1-RM. Two mixed factor design ANOVAs (Group {dollar}\times{dollar} Time {dollar}\times{dollar} Arm) found no significant difference between groups at pretest (.05). All three groups demonstrated significant increases in strength, both isometrically and isotonically. A statistical difference was not found between the three training methods and their effect on isotonic strength. However, the Weight and the NMES/Weight group demonstrated significantly greater gains in isotonic strength than the NMES group. The NMES/Weight group demonstrated the largest mean increase in strength, followed by the Weight group and the NMES group, respectively. The results indicated that groups NMES/Weight and Weight were equivalent. Therefore, the combination of NMES and isotonic weight training has no greater effect on strength than traditional isotonic training