Design of self-healing nanocomposite hydrogels and the application to the detection of human exercise and ascorbic acid in sweat.

Hydrogel sensors have broad application prospects in human motion monitoring and sweat composition detection. However, hydrogel-based sensors are faced with challenges such as low accuracy and poor mechanical properties of analytes detection. Based on mussel-inspired chemistry, we synthesized mesoporous silica@polydopamine-Au (MPS@PDA-Au) nanomaterials and designed a self-healing nanocomposite hydrogel to monitor human movement and ascorbic acid detection in sweat. Mesoporous silica (MPS) possess orderly mesoporous structure. Dopamine (DA) polymerized on the surface of MPS to generate polydopamine (PDA), forming the composite material MPS@PDA-Au. This composite was then embedded into polyvinyl alcohol (PVA) hydrogels through a simple freeze-thaw cycle process. The hydrogels have achieved excellent deformable ability (508.6%), self-healing property (90.5%) and mechanical strength (2.9 MPa). The PVA/MPS@PDA-Au hydrogel sensors had the characteristics of fast response time (123.2 ms), wide strain sensing range (0-500%), excellent fatigue resistance and stability in human detection. The detection range of ascorbic acid (AA) in sweat was wide (8.0 µmol/L-100.0 µmol/L) and the detection limit was low (3.3 µmol/L). Therefore, these hydrogel sensors have outstanding application prospects in human motion monitoring and sweat composition detection.

Feasibility and usefulness of postoperative mobilization goals in the enhanced recovery after surgery (ERAS®) clinical pathway for elective colorectal surgery.

PURPOSE: Despite mobilization is highly recommended in the ERAS® colorectal guideline, studies suggest that more than half of patients don't reach the daily goal of 360 min out of bed. However, data used to quantify mobilization are predominantly based on self-assessments, for which the accuracy is uncertain. This study aims to accurately measure postoperative mobilization in ERAS®-patients by validated motion data from body sensors. METHODS: ERAS®-patients with elective bowel resections were eligible. Self-assessments and motion sensors (movisens: ECG-Move 4 and Move 4; Garmin: Vivosmart4) were used to record mobilization parameter from surgery to postoperative day 3 (POD3): Time out of bed, time on feet and step count. RESULTS: 97 patients were screened and 60 included for study participation. Self-assessment showed a median out of bed duration of 215 min/day (POD1: 135 min, POD2: 225 min, POD3: 225 min). The goal of 360 min was achieved by 16.67% at POD1, 21.28% at POD2 and 20.45% at POD3. Median time on feet objectively measured by Move 4 was 109 min/day. During self-assessment, patients significantly underestimated their "time on feet"-duration with 85 min/day (p = 0.008). Median number of steps was 933/day (Move 4). CONCLUSION: This study confirmed with objectively supported data, that most patients don't reach the daily mobilization goal of 360 min despite being treated by an ERAS®-pathway with ERAS®-nurse. Even considering an empirically approximated underestimation, the ERAS®-target isn't achieved by more than 75% of patients. Therefore, we propose an adjustment of the general ERAS®-goals into more patient-centered, individualized and achievable goals. REGISTRATION: This study is part of the MINT-ERAS-project and was registered prospectively in the German Clinical Trials Register on 25.02.2022. Trial registration number is "DRKS00027863".

The Effect of taVNS at 25 Hz and 100 Hz on Parkinson's Disease Gait-A Randomized Motion Sensor Study.

BACKGROUND: Transcutaneous electrostimulation of the auricular branch of the vagal nerve (taVNS) has the propensity to reach diffuse neuromodulatory networks, which are dysfunctional in Parkinson's disease (PD). Previous studies support the use of taVNS as an add-on treatment for gait in PD. OBJECTIVES: We assessed the effect of taVNS at 25 Hz (taVNS25), taVNS at 100 Hz (taVNS100), and sham earlobe stimulation (sVNS) on levodopa responsive (arm swing velocity, arm range of motion, stride length, gait speed) and non-responsive gait characteristics (arm range of motion asymmetry, anticipatory postural adjustment [APA] duration, APA first step duration, APA first step range of motion), and turns (first turn duration, double 360° turn duration, steps per turn) in advanced PD. METHODS: In our double blind sham controlled within-subject randomized trial, we included 30 PD patients (modified Hoehn and Yahr stage, 2.5-4) to assess the effect of taVNS25, taVNS100, and sVNS on gait characteristics measured with inertial motion sensors during the instrumented stand and walk test and a double 360° turn. Separate generalized mixed models were built for each gait characteristic. RESULTS: During taVNS100 compared to sVNS arm swing velocity (P = 0.030) and stride length increased (P = 0.027), and APA duration decreased (P = 0.050). During taVNS25 compared to sVNS stride length (P = 0.024) and gait speed (P = 0.021) increased and double 360° turn duration decreased (P = 0.039). CONCLUSIONS: We have found that taVNS has a frequency specific propensity to improve stride length, arm swing velocity, and gait speed and double 360° turn duration in PD patients. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Influence of motivational interviewing on postoperative mobilization in the enhanced recovery after surgery (ERAS®) pathway in elective colorectal surgery - a randomized patient-blinded pilot study.

PURPOSE: Early mobilization is an essential component of the Enhanced Recovery after Surgery (ERAS®)-pathway. However, a large percentage of patients fail to achieve the ERAS® recommended goal (360 min out of bed from post-operative day 1/POD1). Motivational Interviewing (MI) is an evidence-based type of patient-centered consultation to promote intrinsic motivation. This study aims to evaluate if MI can improve postoperative mobilization. METHODS: This two-arm, patient-blinded pilot randomized controlled trial included ERAS®-patients undergoing elective bowel resections. Conversations were validated by MI Treatment Integrity. Two validated motion sensors (movisens) and self-assessments were used to measure mobilization (POD1-POD3: Time out of bed, time on feet and step count). RESULTS: 97 patients were screened, 60 finally included and randomized. Cumulatively across POD1-3, the intervention group (IG) was longer out of bed than the control group (CG) (median: 685 vs. 420 min; p=0.022). The IG achieved the ERAS®-goal of 360 min/day more frequently across POD1-3 (27.4% vs. 10.61%; p=0.013). Time on feet was 131.5 min/day (median per POD) in IG vs. 95.8 min/day in the CG (p=0.212), step count was 1347 in IG vs. 754 steps/day in CG (p=0.298). CONCLUSION: MI could be conducted low threshold and was well accepted by patients. MI can improve mobilization in the context of ERAS®. Despite better performance, it should be noted that only 27.4% of the IG reached the ERAS®-compliance goal of 360 min/day. The findings of this pilot study stipulate to further test the promising perioperative effects of MI within a multicenter superiority trial. REGISTRATION: This study was registered prospectively in the German Clinical Trials Register on 25.02.2022. Trial registration number is "DRKS00027863".

Objectively assessed sleep quality parameters in Multiple Sclerosis at home: Association to disease, disease severity and physical activity.

BACKGROUND: Multiple Sclerosis (MS) is a chronic inflammatory autoimmune, neurodegenerative disease that affects regular mobility and leads predominantly to physical disability. Poor sleep quality, commonly reported in MS patients, impacts their physical activity (PA). Accelerometers monitor 24-h activity patterns, offering insights into disease progression in daily life. OBJECTIVE: To test if the sleep quality variables of MS patients, as assessed with wrist-worn accelerometers, differ from those of controls and are associated with PA and disease severity variables. METHODS: Seven-day raw accelerometer data collected from 40 MS patients and 24 controls was processed using an open-source GGIR package, from which variables of sleep quality (sleep efficiency, wake after sleep onset (WASO), sleep regularity index (SRI), intradaily variability (IV)) and PA (of different intensities: inactivity, light (LPA), moderate (MPA), vigorous (VPA)) were analyzed. The variables were compared between the two study groups and in MS patients, correlation tested associations among the variables of sleep quality, PA, and disease severity (assessed with the Expanded Disability Status Scale, EDSS). RESULTS: Sleep efficiency was the only variable that differed significantly between MS patients and controls (lower in MS, p = 0.01). Both SRI (positively) and IV (negatively) correlated with the time spent in LPA and MPA. WASO correlated negatively with inactivity. CONCLUSION: This is one of the few studies with a wrist-worn accelerometer that shows a difference in sleep efficiency between MS patients and controls and, in MS, an association of sleep quality variables with PA variables.

Playing Flappy Bird Based on Motion Recognition Using a Transformer Model and LIDAR Sensor.

A transformer neural network is employed in the present study to predict Q-values in a simulated environment using reinforcement learning techniques. The goal is to teach an agent to navigate and excel in the Flappy Bird game, which became a popular model for control in machine learning approaches. Unlike most top existing approaches that use the game's rendered image as input, our main contribution lies in using sensory input from LIDAR, which is represented by the ray casting method. Specifically, we focus on understanding the temporal context of measurements from a ray casting perspective and optimizing potentially risky behavior by considering the degree of the approach to objects identified as obstacles. The agent learned to use the measurements from ray casting to avoid collisions with obstacles. Our model substantially outperforms related approaches. Going forward, we aim to apply this approach in real-world scenarios.

A Model of Multi-Finger Coordination in Keystroke Movement.

In multi-finger coordinated keystroke actions by professional pianists, movements are precisely regulated by multiple motor neural centers, exhibiting a certain degree of coordination in finger motions. This coordination enhances the flexibility and efficiency of professional pianists' keystrokes. Research on the coordination of keystrokes in professional pianists is of great significance for guiding the movements of piano beginners and the motion planning of exoskeleton robots, among other fields. Currently, research on the coordination of multi-finger piano keystroke actions is still in its infancy. Scholars primarily focus on phenomenological analysis and theoretical description, which lack accurate and practical modeling methods. Considering that the tendon of the ring finger is closely connected to adjacent fingers, resulting in limited flexibility in its movement, this study concentrates on coordinated keystrokes involving the middle and ring fingers. A motion measurement platform is constructed, and Leap Motion is used to collect data from 12 professional pianists. A universal model applicable to multiple individuals for multi-finger coordination in keystroke actions based on the backpropagation (BP) neural network is proposed, which is optimized using a genetic algorithm (GA) and a sparrow search algorithm (SSA). The angular rotation of the ring finger's MCP joint is selected as the model output, while the individual difference information and the angular data of the middle finger's MCP joint serve as inputs. The individual difference information used in this study includes ring finger length, middle finger length, and years of piano training. The results indicate that the proposed SSA-BP neural network-based model demonstrates superior predictive accuracy, with a root mean square error of 4.8328°. Based on this model, the keystroke motion of the ring finger's MCP joint can be accurately predicted from the middle finger's keystroke motion information, offering an evaluative method and scientific guidance for the training of multi-finger coordinated keystrokes in piano learners.

High Signal-to-Noise Ratio Event-Driven MEMS Motion Sensing.

Two solutions for improving MEMS triboelectric vibration sensors performance in contact-separation mode are reported experimentally and analytically. Triboelectric sensors have mostly been studied in the mesoscale. The gap variation between the electrodes induces a potential difference that represents the external vibration. Miniaturizing the device limits the sensor output because of the limited gap. This work offers a warped MEMS diaphragm constrained on its edges. The dome-shaped structure provides one order of magnitude larger displacement after contact-separation than standard designs resulting in one order of magnitude greater voltage and signal-to-noise-ratio. Second, micro triboelectric sensors do not operate unless the external vibration is sufficiently forceful to initiate contact between layers. The proposed constraints on the edge of the diaphragm provide friction during periodic motion and generate charges. The combination of the warped diaphragm and boundary constraints instead of serpentine springs increases the charge density and voltage generation. The mechanical properties and electrical output are thoroughly investigated including nonlinearity, sensitivity, and signal-to-noise ratio. A sensitivity of 250 mV g-1 and signal-to-noise-ratio of 32 dB is provided by the presented device at resonance, which is very promising for event-driven motion sensors because it does not require signal conditioning and therefore simplifies the sensing circuitry.

Measuring ergonomic interventions and prevention programs for reducing musculoskeletal injury risk in the dental workforce: A systematic review.

PURPOSE: Pain from musculoskeletal disorders (MSD) is experienced by all types of dental clinicians, leading to forced reduction of work capacity and premature retirement. Prolonged static posture is a major contributor to MSD in dentistry. Currently, there is no uniform ergonomic training in dentistry to prevent MSD. This systematic review explored and summarized methods and technologies for baseline and ongoing measurements of ergonomic intervention and prevention programs for reducing postural risk and for prevention of MSDs. METHODS: The review was conducted in accordance with PRISMA guidelines. The literature search included PUBMED, Embase, CINAHL and the Cochrane Library, and identified relevant observational, experimental, quasi-experimental, research, and intervention studies. Quality was rated using a validated standardized instrument. RESULTS: A total of 20 studies were included, of which 4 were rated as being of the high quality. Methods ranged from observers to motion sensors. Ten studies focused used established measuring tools (PAI, RULA, REBA), while nine studies focused on effectiveness of alternative methods of measurement. One study identified ISO standard 11226 for baseline comparisons. Evolving technologies were found to be suitable for baseline measurements of posture, and for feedback for developing sustainable postural behaviors. No studies measured ongoing postural performance improvement. CONCLUSIONS: This systematic review adds to current evidence for the expanded and improved use of early training and testing of postural competence to reduce MSD risk among dental professionals. Further studies using technologies to assess the impacts of ergonomic training and testing among dental professionals are warranted.

Case study: Gait assessment of a patient with hallux rigidus before and after plantar modification.

INTRODUCTION AND IMPORTANCE: Hallux rigidus (HR) is a degenerative arthritis affecting the first metatarsophalangeal joint (MTP), leading to pain and functional impairment, particularly during the propulsive phase of walking. The prevalence of HR is about 2.5 % in individuals over 50, but younger individuals can also be affected, as demonstrated in this case. CASE PRESENTATION: We report the case of a 26-year-old patient with a body mass index (BMI) of 20.2, who has been suffering from HR for 5 years. The patient presented with walking difficulties, characterized by a limp and impaired propulsion phase, and pain in the right foot due to HR. A comprehensive gait assessment was conducted using a baropodometric platform and integrated smartphone motion sensors. Following the diagnosis, a non-surgical intervention involving the application of a compressed cotton felt foot orthosis at the MTP plantar area was initiated. This intervention aimed to alleviate pain and improve the functional mobility of the right big toe. Post-treatment assessments showed an increase in the big toe's mobility from 0 degrees to 35 degrees, as measured by a digital goniometer. CLINICAL DISCUSSION: The application of a soft support, such as compressed cotton felt, at the plantar area of MTP, demonstrated a potential non-surgical therapeutic approach to improve gait and reduce discomfort in HR patients. CONCLUSION: This case study underscores the potential benefits of plantar modification in the management of HR.

Hidden Markov models for presence detection based on CO2 fluctuations.

Presence sensing systems are gaining importance and are utilized in various contexts such as smart homes, Ambient Assisted Living (AAL) and surveillance technology. Typically, these systems utilize motion sensors or cameras that have a limited field of view, leading to potential monitoring gaps within a room. However, humans release carbon dioxide (CO2) through respiration which spreads within an enclosed space. Consequently, an observable rise in CO2 concentration is noted when one or more individuals are present in a room. This study examines an approach to detect the presence or absence of individuals indoors by analyzing the ambient air's CO2 concentration using simple Markov Chain Models. The proposed scheme achieved an accuracy of up to 97% in both experimental and real data demonstrating its efficacy in practical scenarios.

Effectiveness of biofeedback-assisted asynchronous telerehabilitation in musculoskeletal care: A systematic review.

Background: Musculoskeletal conditions are the leading cause of disability worldwide. Telerehabilitation may be a viable option in the management of these conditions, facilitating access and patient adherence. Nevertheless, the impact of biofeedback-assisted asynchronous telerehabilitation remains unknown. Objective: To systematically review and assess the effectiveness of exercise-based asynchronous biofeedback-assisted telerehabilitation on pain and function in individuals with musculoskeletal conditions. Methods: This systematic review followed Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The search was conducted using three databases: PubMed, Scopus, and PEDro. Study criteria included articles written in English and published from January 2017 to August 2022, reporting interventional trials evaluating exercise-based asynchronous telerehabilitation using biofeedback in adults with musculoskeletal disorders. The risks of bias and certainty of evidence were appraised using the Cochrane tool and Grading of Recommendations, Assessment, Development, and Evaluation (GRADE), respectively. The results are narratively summarized, and the effect sizes of the main outcomes were calculated. Results: Fourteen trials were included: 10 using motion tracker technology (N = 1284) and four with camera-based biofeedback (N = 467). Telerehabilitation with motion trackers yields at least similar improvements in pain and function in people with musculoskeletal conditions (effect sizes: 0.19-1.45; low certainty of evidence). Uncertain evidence exists for the effectiveness of camera-based telerehabilitation (effect sizes: 0.11-0.13; very low evidence). No study found superior results in a control group. Conclusions: Asynchronous telerehabilitation may be an option in the management of musculoskeletal conditions. Considering its potential for scalability and access democratization, additional high-quality research is needed to address long-term outcomes, comparativeness, and cost-effectiveness and identify treatment responders.

On the Use of a Convolutional Block Attention Module in Deep Learning-Based Human Activity Recognition with Motion Sensors.

Sensor-based human activity recognition with wearable devices has captured the attention of researchers in the last decade. The possibility of collecting large sets of data from various sensors in different body parts, automatic feature extraction, and aiming to recognize more complex activities have led to a rapid increase in the use of deep learning models in the field. More recently, using attention-based models for dynamically fine-tuning the model features and, in turn, improving the model performance has been investigated. However, the impact of using channel, spatial, or combined attention methods of the convolutional block attention module (CBAM) on the high-performing DeepConvLSTM model, a hybrid model proposed for sensor-based human activity recognition, has yet to be studied. Additionally, since wearables have limited resources, analysing the parameter requirements of attention modules can serve as an indicator for optimizing resource consumption. In this study, we explored the performance of CBAM on the DeepConvLSTM architecture both in terms of recognition performance and the number of additional parameters required by attention modules. In this direction, the effect of channel and spatial attention, individually and in combination, were examined. To evaluate the model performance, the Pamap2 dataset containing 12 daily activities and the Opportunity dataset with its 18 micro activities were utilized. The results showed that the performance for Opportunity increased from 0.74 to 0.77 in the macro f1-score owing to spatial attention, while for Pamap2, the performance increased from 0.95 to 0.96 owing to the channel attention applied to DeepConvLSTM with a negligible number of additional parameters. Moreover, when the activity-based results were analysed, it was observed that the attention mechanism increased the performance of the activities with the worst performance in the baseline model without attention. We present a comparison with related studies that use the same datasets and show that we could achieve higher scores on both datasets by combining CBAM and DeepConvLSTM.

Postsurgical Analysis of Gait, Radiological, and Functional Outcomes in Children with Developmental Dysplasia of the Hip.

BACKGROUND: Children undergoing DDH correction surgery may experience gait abnormalities following soft tissue releases and bony procedures. The purpose of this study was to compare the residual gait changes, radiological outcomes, and functional outcomes in children who underwent DDH surgery with those in healthy controls. METHODS: Inertial motion sensors were used to record the gait of 14 children with DDH and 14 healthy children. Pelvic X-ray was performed to determine the Severin classification and the presence of femoral head osteonecrosis (Bucholz-Odgen classification). For functional evaluation, the Children's Hospital Oakland Hip Evaluation Scale (CHOHES) was used. RESULTS: There was no difference in spatial parameters between the two groups. In terms of temporal parameters, the DDH-affected limbs had a shorter stance phase (p < 0.001) and a longer swing phase (p < 0.001) than the control group. The kinematic study showed that the affected limb group had smaller hip adduction angle (p = 0.002) and increased internal rotation (p = 0.006) with reduced upward pelvic tilt (p = 0.020). Osteonecrosis was graded II, III, and IV in five, three, and one patients, respectively. Five patients had no AVN changes. The Severin classification was grade I, II, and III for six, three, and five patients, respectively. Most patients had good functional outcomes on the CHOHES, with a mean total score of 96.64 ± 5.719. Multivariate regression analysis revealed that weight, height, and femoral osteotomy were independent predictors for gait, radiological and functional outcome. CONCLUSION: Despite good functional scores overall, some children had poor radiological outcomes and gait abnormalities. Our results identified the risk factors for poor outcomes, and we recommend specified rehabilitative strategies for long-term management.

Advancing Reverse Electrowetting-on-Dielectric from Planar to Rough Surface Electrodes for High Power Density Energy Harvesting.

Reverse electrowetting-on-dielectric (REWOD)-based energy harvesting has been studied over the last decade as a novel technique of harvesting energy by actuating liquid droplet(s) utilizing applied mechanical modulation. Much prior research in REWOD has relied on planar electrodes, which by its geometry possess a limited surface area. In addition, most of the prior REWOD works have applied a high bias voltage to enhance the output power that compromises the concept of self-powering wearable motion sensors in human health monitoring applications. In order to enhance the REWOD power density resulting from an increased electrode-electrolyte interfacial area, high surface area electrodes are required. Herein, electrical and multiphysics-based modeling approaches of REWOD energy harvester using structured rough surface electrodes are presented. By enhancing the overall available surface area, an increase in the overall capacitance is achieved. COMSOL and MATLAB-based models are also developed, and the empirical results are compared with the models to validate the performance. Root mean square (RMS) power density is calculated using the RMS voltage across an optimal load impedance. For the proposed rough electrode REWOD energy harvester, maximum power density of 3.18 µW cm-2 is achieved at 5 Hz frequency, which is ≈4 times higher than that of the planar electrodes.

Augmented PIN Authentication through Behavioral Biometrics.

Personal Identification Numbers (PINs) are widely used today for user authentication on mobile devices. However, this authentication method can be subject to several attacks such as phishing, smudge, and side-channel. In this paper, we increase the security of PIN-based authentication by considering behavioral biometrics, specifically the smartphone movements typical of each user. To this end, we propose a method based on anomaly detection that is capable of recognizing whether the PIN is inserted by the smartphone owner or by an attacker. This decision is taken according to the smartphone movements, which are recorded during the PIN insertion through the built-in motion sensors. For each digit in the PIN, an anomaly score is computed using Machine Learning (ML) techniques. Subsequently, these scores are combined to obtain the final decision metric. Numerical results show that our authentication method can achieve an Equal Error Rate (EER) as low as 5% in the case of 4-digit PINs, and 4% in the case of 6-digit PINs. Considering a reduced training set, composed of solely 50 samples, the EER only slightly worsens, reaching 6%. The practicality of our approach is further confirmed by the low processing time required, on the order of fractions of milliseconds.

Strategies to Enhance the Data Density in Synchronous Electromagnetic Encoders.

In this paper, we report two different strategies to enhance the data density in electromagnetic encoders with synchronous reading. One approach uses a periodic chain of rectangular metallic patches (clock chain) that determines the encoder velocity, and dictates the instants of time for retrieving the bits of the identification (ID) code. However, contrary to previous electromagnetic encoders, the ID is inferred at both the rising and the falling edges of the clock signal generated by the clock chain. Moreover, the bits of information are not given by the presence or absence of metallic patches at their predefined positions in the so-called ID code chain. With this novel encoding system, a bit state corresponding to a certain instant of time is identical to the previous bit state, unless there is a change in the envelope function of the ID code signal, determined by the additional non-periodic ID code chain. The other encoding strategy utilizes a single chain of C-shaped resonators, and encoding is achieved by considering four different resonator dimensions, corresponding to four states and, hence, to two bits per resonator of the chain. Thus, with these two strategies, the data density is twice the one achievable in previously reported synchronous electromagnetic encoders.

Nanocomposite conductive tough hydrogel based on metal coordination reinforced covalent Pluronic F-127 micelle network for human motion sensing.

The design of conductive hydrogels integrating anti-fatigue, high sensitivity, strong mechanical property and good sterilization performance remains a challenge. We innovatively introduced metal coordination in covalently crosslinked Pluronic F-127 micelle network and synthesized nanocomposite conductive tough hydrogel through the combination of covalent crosslinking, metal coordination and silver nanowire reinforcement. Compared with pure diacylated PF127 hydrogel (PF127), the tensile strength of PF-AA-AM-Al3+/Ag0.25 hydrogel reaching 1.4 MPa was about 10 times than that of PF127. The toughness of PF-AA-AM-Al3+/Ag0.25 reaches 1.88 MJ/m3. Compared with PF-AA-AM-Al3+, the introduction of silver nanowires increased the fatigue life of PF-AA-AM-Al3+/Ag0.25 by 200% (31837 cycles), 170% (12804 cycles) and 1022% (511 cycles) under 100%, 120% and 150% ultimate tensile strains, respectively. Besides, the PF-AA-AM-Al3+/Ag0.25 showed strain sensitivity to small deformation (Gauge factor = 2.42) in wearable tests on hands and knees. In addition, the PF-AA-AM-Al3+/Ag0.25 had good cytocompatibility and antibacterial performance that bacteria killing ratio of 98% to S. aureus and 99% to E. coli. Finally, a viscoelastic numerical constitutive model was established based on finite element method to study the damage failure history of the material. Comparative analysis showed that local stress concentration was the main factor leading to the failure of hydrogel.

Internet-based vestibular rehabilitation versus standard care after acute onset vertigo: a study protocol for a randomized controlled trial.

BACKGROUND: Dizziness and vertigo affect around 15% of adults annually and represent common reasons for contacting health services, accounting for around 3% of all emergency department visits worldwide. Vertigo is also associated with excessive use of diagnostic imaging and emergency care and decreased productivity, primarily because of work absenteeism. Vestibular rehabilitation is an evidence-based treatment for chronic dizziness and supervised group exercise therapy has recently been shown to be effective after vestibular neuritis, a common cause of acute onset vertigo. However, such interventions are not readily available and there is a need for more easily accessible tools. The purpose of this study is to investigate the effects on vestibular symptoms of a 6-week online vestibular rehabilitation tool after acute onset vertigo, with the aim of aiding vestibular rehabilitation by presenting a more accessible tool that can help to reduce recovery time. METHODS: Three hundred twenty individuals diagnosed with acute vestibular syndrome (AVS) will be recruited from multiple hospitals in Sweden and the effects of an online vestibular rehabilitation tool, YrselTräning, on vestibular symptoms after acute onset vertigo will be compared to standard care (written instructions leaflet) in a two-armed, evaluator-blinded, multicenter randomized controlled trial. The primary outcome will be the Vertigo Symptom Scale Short Form (VSS-SF) score at 6 weeks after symptom onset. Secondary outcomes include effects of the intervention on activities of daily living, mood and anxiety, vestibular function recovery, mobility measures, health economic effects, and the reliability of the Swedish VSS-SF translation. DISCUSSION: Participants using the online vestibular rehabilitation tool are expected to recover earlier and to a greater extent from their symptoms as compared to standard care. Since up to 50% of people with AVS without treatment develop persistent symptoms, effective treatment of AVS will likely lead to a higher quality of life and help reduce the societal costs associated with dizziness and vertigo. TRIAL REGISTRATION: Clinicaltrials.gov NCT05056324 . Registered on September 24, 2021.

Bogazici university smartphone accelerometer sensor dataset.

Mobile devices especially smartphones have gained high popularity and become a part of daily life in recent years. Therefore, there are many studies that investigate users' interactions with smartphones and try to extract meaningful information from various inputs. Actually, the main motivation behind these studies is the behavioral differences of users in their interactions with smartphones. In these studies, motion sensors in devices such as accelerometer and gyroscope are widely used. Data obtained from motion sensors allows to detect information such as age-group, gender, activity type, identity of users. In this context, we develop an Android application that gathers accelerometer sensor data while users perform different activities. This application records all accelerometer data and touch event information generated while users are using their devices. Then, we perform two experiments and collect two different data using this application. In the first experiment, we collect data from 107 child users and 100 adult users to analyze the impact of different age-groups' behavior on sensor data. This dataset includes more than 11.000 taps data for child and adult users, in total. In the second experiment, data is collected from 60 female and 60 male users for different activities like sitting and walking. There are more than 6.000 taps data for sitting and walking scenarios separately in the second dataset. This dataset makes it possible to analyze the changes created by different gender and activity types in the sensor data. These data can be used for behavioral biometric analyses on smartphones such as user age-group and gender detection, user identification and authentication.