Breathing better: A tech-monitored study of positive expiratory pressure and reading aloud for chronic obstructive pulmonary disease

BACKGROUND

Breathing exercises, such as diaphragmatic breathing and positive expiratory pressure (PEP), relieve breathlessness in people with chronic obstructive pulmonary disease (COPD).

AIM

This study aimed to investigate the effects of breathing exercises with PEP and reading aloud on vital signs, fatigue level, severity of dyspnoea and respiratory function parameters in patients with COPD.

DESIGN

The study followed a randomized controlled trial of COPD patients from a single hospital in eastern Turkey.

METHODS

The study included 103 patients who were randomly assigned to receive pre-reading exercises, breathing exercises with a PEP device or no intervention for 8 weeks.

RESULTS

The use of a PEP device improved oxygen saturation, forced expiratory volume in 1 s (FEV1 ) and FEV1 /forced vital capacity (FVC) values and reduced fatigue and dyspnoea severity. Reading aloud lowered the mean arterial pressure and reduced fatigue and dyspnoea severity.

CONCLUSION

The study concludes that PEP devices and reading aloud can improve respiratory function in patients with COPD. Additionally, reading aloud is an accessible, easy-to-implement and economically feasible method for treating COPD symptoms.

Diagnostic Value of a Wearable Continuous Electrocardiogram Monitoring Device (AT-Patch) for New-Onset Atrial Fibrillation in High-Risk Patients: Prospective Cohort Study

BACKGROUND

While conventional electrocardiogram monitoring devices are useful for detecting atrial fibrillation, they have considerable drawbacks, including a short monitoring duration and invasive device implantation. The use of patch-type devices circumvents these drawbacks and has shown comparable diagnostic capability for the early detection of atrial fibrillation.

OBJECTIVE

We aimed to determine whether a patch-type device (AT-Patch) applied to patients with a high risk of new-onset atrial fibrillation defined by the congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, stroke, vascular disease, age 65-74 years, sex scale (CHA2DS2-VASc) score had increased detection rates.

METHODS

In this nonrandomized multicenter prospective cohort study, we enrolled 320 adults aged ≥19 years who had never experienced atrial fibrillation and whose CHA2DS2-VASc score was ≥2. The AT-Patch was attached to each individual for 11 days, and the data were analyzed for arrhythmic events by 2 independent cardiologists.

RESULTS

Atrial fibrillation was detected by the AT-Patch in 3.4% (11/320) of patients, as diagnosed by both cardiologists. Interestingly, when participants with or without atrial fibrillation were compared, a previous history of heart failure was significantly more common in the atrial fibrillation group (n=4/11, 36.4% vs n=16/309, 5.2%, respectively; P=.003). When a CHA2DS2-VASc score ≥4 was combined with previous heart failure, the detection rate was significantly increased to 24.4%. Comparison of the recorded electrocardiogram data revealed that supraventricular and ventricular ectopic rhythms were significantly more frequent in the new-onset atrial fibrillation group compared with nonatrial fibrillation group (3.4% vs 0.4%; P=.001 and 5.2% vs 1.2%; P<.001), respectively.

CONCLUSIONS

This study detected a moderate number of new-onset atrial fibrillations in high-risk patients using the AT-Patch device. Further studies will aim to investigate the value of early detection of atrial fibrillation, particularly in patients with heart failure as a means of reducing adverse clinical outcomes of atrial fibrillation.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04857268; https://classic.clinicaltrials.gov/ct2/show/NCT04857268.

ECG Matching: An Approach to Synchronize ECG Datasets for Data Quality Comparisons

Clinical assessment of newly developed sensors is important for ensuring their validity. Comparing recordings of emerging electrocardiography (ECG) systems to a reference ECG system requires accurate synchronization of data from both devices. Current methods can be inefficient and prone to errors. To address this issue, three algorithms are presented to synchronize two ECG time series from different recording systems: Binned R-peak Correlation, R-R Interval Correlation, and Average R-peak Distance. These algorithms reduce ECG data to their cyclic features, mitigating inefficiencies and minimizing discrepancies between different recording systems. We evaluate the performance of these algorithms using high-quality data and then assess their robustness after manipulating the R-peaks. Our results show that R-R Interval Correlation was the most efficient, whereas the Average R-peak Distance and Binned R-peak Correlation were more robust against noisy data.

Spectral Fusion of Heartbeat and Accelerometer Data for Estimation of Breathing Rate in Wearable Patches

Despite developments in wearable devices for detecting various bio-signals, continuous measurement of breathing rate (BR) remains a challenge. This work presents an early proof of concept that employs a wearable patch to estimate BR. We propose combining techniques for calculating BR from electrocardiogram (ECG) and accelerometer (ACC) signals, while applying decision rules based on signal-to-noise (SNR) to fuse the estimates for improved accuracy.

Wearable and Invisible Sensor Design for Eye-Motion Monitoring Based on Ferrofluid and Electromagnetic Sensing Technologies

For many human body diseases, treatments in the early stages are more efficient and safer than those in the later stages; therefore, detecting the early symptoms of a disease is crucial. One of the most significant early indicators for diseases is bio-mechanical motion. This paper provides a unique way of monitoring bio-mechanical eye motion based on electromagnetic sensing technology and a ferro-magnetic material, ferrofluid. The proposed monitoring method has the advantages of being inexpensive, non-invasive, sensor-invisible and extremely effective. Most of the medical devices are cumbersome and bulky, which makes them hard to apply for daily monitoring. However, the proposed eye-motion monitoring method is designed based on ferrofluid eye make-up and invisible sensors embedded inside the frame of glasses such that the system is wearable for daily monitoring. In addition, it has no influence on the appearance of the patient, which is beneficial for the mental health of some patients who do not want to attract public attention during treatment. The sensor responses are modelled using finite element simulation models, and wearable sensor systems are created. The designed frame of the glasses is manufactured based on 3-D printing technology. Experiments are conducted to monitor eye bio-mechanical motions, such as the frequency of eye blinking. Both the quick blinking behaviour with an overall frequency of around 1.1 Hz and the slow blinking behaviour with an overall frequency of around 0.4 Hz can be observed through experimentation. Simulations and measurements results show that the proposed sensor design can be employed for bio-mechanical eye-motion monitoring. In addition, the proposed system has the advantages of invisible sensor set-up and will not affect the appearance of the patient, which is not only convenient for the daily life of the patient but also beneficial for mental health.

Sedation/agitation monitoring using a wearable device for ventilated children

BACKGROUND

Developing continuous and labor-saving sedation/agitation monitoring methods in ventilated children is important to avoid undesirable events such as unplanned extubation. The existing scales are often challenging to use. We therefore aimed to evaluate the feasibility of sedation/agitation monitoring using a wearable device with a built-in accelerometer for ventilated children.

METHODS

This prospective observational pilot study included children aged 15 years or less, admitted to the pediatric intensive care unit on mechanical ventilation after cardiac catheterization between December 2021 and April 2022. The wearable device with a built-in accelerometer was attached to either of the upper limbs, and accelerations due to upper limb movements were measured for 2 h after admission or until extubation, whichever was earliest. Accelerations were measured at 0.02 s intervals, with the mean acceleration calculated for each 1 min interval. The State Behavioral Scale (SBS) was completed at 1 min intervals, with the SBS score (-1, 0, 1, or 2) compared with the mean acceleration.

RESULTS

The study included 20 children with a median age of 12 months. The mean accelerations and SBS scores were positively correlated (Kendall's τ, 0.22; p < 0.001), with an increase in the median (interquartile range) acceleration from an SBS score of -1 through 2, as follows: SBS -1, 0.200 (0.151-0.232) m/s2 ; SBS 0, 0.202 (0.190-0.235) m/s2 ; SBS, 1, 0.312 (0.236-0.427) m/s2 ; SBS 2, 0.455 (0.332-0.517) m/s2 . No adverse events were observed.

CONCLUSIONS

This study showed that continuous, labor-saving sedation/agitation monitoring of ventilated children was feasible using a wearable device with a built-in accelerometer.

Using Wearable Cameras to Investigate Health-Related Daily Life Experiences: A Literature Review of Precautions and Risks in Empirical Studies

Automated, wearable cameras can benefit health-related research by capturing accurate and objective information about individuals' daily experiences. However, wearable cameras present unique privacy- and confidentiality-related risks due to the possibility of the images capturing identifying or sensitive information from participants and third parties. Although best practice guidelines for ethical research with wearable cameras have been published, limited information exists on the risks of studies using wearable cameras. The aim of this literature review was to survey risks related to using wearable cameras, and precautions taken to reduce those risks, as reported in empirical research. Forty-five publications, comprising 36 independent studies, were reviewed, and findings revealed that participants' primary concerns with using wearable cameras included physical inconvenience and discomfort in certain situations (e.g., public settings). None of the studies reviewed reported any serious adverse events. Although it is possible that reported findings do not include all risks experienced by participants in research with wearable cameras, our findings suggest a low level of risk to participants. However, it is important that investigators adopt recommended precautions, which can promote autonomy and reduce risks, including participant discomfort.

Comparison of wireless and wired alarm devices for nocturnal enuresis treatment

BACKGROUND

In monosymptomatic nocturnal enuresis (MNE) treatment, enuretic alarm devices are the first recommended treatment option. This study aimed to compare retrospectively the effectiveness of wearable wireless and wired alarm devices for MNE treatment in children aged 6-14 years.

METHODS

All children aged 6-16 with MNE who underwent alarm therapy as outpatients were included. A wired alarm device was used from 2012 to 2015, and a wireless alarm device was used from 2016 to 2019. The primary outcomes were the dropout rates during therapy and at last follow up. The full response(14 consecutive dry nights) and the partial response rate during therapy were also assessed.

RESULTS

Of the 173 patients enrolled, 75 and 98 used a wired and a wireless alarm device, respectively. The dropout rate at the last visit was significantly lower in the wireless alarm group than that in the wired alarm group (6.1% vs. 20.0%; P = 0.006). The full response(FR) rate was significantly higher in the wireless alarm group than these in the wired alarm group at 4, 12, 24 weeks (4 weeks: 11.2% vs. 1.3%, P = 0.011; 12 weeks: 31.9% vs. 13.5%, P = 0.005; 24 weeks: 72.9% vs. 39.7%, P < 0.0001).

CONCLUSIONS

Wireless alarm therapy for MNE had lower attrition rates and a higher rate of FR than wired alarm therapy.

A Technology-Based Pregnancy Health and Wellness Intervention (Two Happy Hearts): Case Study

BACKGROUND

The physical and emotional well-being of women is critical for healthy pregnancy and birth outcomes. The Two Happy Hearts intervention is a personalized mind-body program coached by community health workers that includes monitoring and reflecting on personal health, as well as practicing stress management strategies such as mindful breathing and movement.

OBJECTIVE

The aims of this study are to (1) test the daily use of a wearable device to objectively measure physical and emotional well-being along with subjective assessments during pregnancy, and (2) explore the user's engagement with the Two Happy Hearts intervention prototype, as well as understand their experiences with various intervention components.

METHODS

A case study with a mixed design was used. We recruited a 29-year-old woman at 33 weeks of gestation with a singleton pregnancy. She had no medical complications or physical restrictions, and she was enrolled in the Medi-Cal public health insurance plan. The participant engaged in the Two Happy Hearts intervention prototype from her third trimester until delivery. The Oura smart ring was used to continuously monitor objective physical and emotional states, such as resting heart rate, resting heart rate variability, sleep, and physical activity. In addition, the participant self-reported her physical and emotional health using the Two Happy Hearts mobile app-based 24-hour recall surveys (sleep quality and level of physical activity) and ecological momentary assessment (positive and negative emotions), as well as the Perceived Stress Scale, Center for Epidemiologic Studies Depression Scale, and State-Trait Anxiety Inventory. Engagement with the Two Happy Hearts intervention was recorded via both the smart ring and phone app, and user experiences were collected via Research Electronic Data Capture satisfaction surveys. Objective data from the Oura ring and subjective data on physical and emotional health were described. Regression plots and Pearson correlations between the objective and subjective data were presented, and content analysis was performed for the qualitative data.

RESULTS

Decreased resting heart rate was significantly correlated with increased heart rate variability (r=-0.92, P<.001). We found significant associations between self-reported responses and Oura ring measures: (1) positive emotions and heart rate variability (r=0.54, P<.001), (2) sleep quality and sleep score (r=0.52, P<.001), and (3) physical activity and step count (r=0.77, P<.001). In addition, deep sleep appeared to increase as light and rapid eye movement sleep decreased. The psychological measures of stress, depression, and anxiety appeared to decrease from baseline to post intervention. Furthermore, the participant had a high completion rate of the components of the Two Happy Hearts intervention prototype and shared several positive experiences, such as an increased self-efficacy and a normal delivery.

CONCLUSIONS

The Two Happy Hearts intervention prototype shows promise for potential use by underserved pregnant women.

Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling

Background

Depression is a prevalent mental disorder that is undiagnosed and untreated in half of all cases. Wearable activity trackers collect fine-grained sensor data characterizing the behavior and physiology of users (ie, digital biomarkers), which could be used for timely, unobtrusive, and scalable depression screening.

Objective

The aim of this study was to examine the predictive ability of digital biomarkers, based on sensor data from consumer-grade wearables, to detect risk of depression in a working population.

Methods

This was a cross-sectional study of 290 healthy working adults. Participants wore Fitbit Charge 2 devices for 14 consecutive days and completed a health survey, including screening for depressive symptoms using the 9-item Patient Health Questionnaire (PHQ-9), at baseline and 2 weeks later. We extracted a range of known and novel digital biomarkers characterizing physical activity, sleep patterns, and circadian rhythms from wearables using steps, heart rate, energy expenditure, and sleep data. Associations between severity of depressive symptoms and digital biomarkers were examined with Spearman correlation and multiple regression analyses adjusted for potential confounders, including sociodemographic characteristics, alcohol consumption, smoking, self-rated health, subjective sleep characteristics, and loneliness. Supervised machine learning with statistically selected digital biomarkers was used to predict risk of depression (ie, symptom severity and screening status). We used varying cutoff scores from an acceptable PHQ-9 score range to define the depression group and different subsamples for classification, while the set of statistically selected digital biomarkers remained the same. For the performance evaluation, we used k-fold cross-validation and obtained accuracy measures from the holdout folds.

Results

A total of 267 participants were included in the analysis. The mean age of the participants was 33 (SD 8.6, range 21-64) years. Out of 267 participants, there was a mild female bias displayed (n=170, 63.7%). The majority of the participants were Chinese (n=211, 79.0%), single (n=163, 61.0%), and had a university degree (n=238, 89.1%). We found that a greater severity of depressive symptoms was robustly associated with greater variation of nighttime heart rate between 2 AM and 4 AM and between 4 AM and 6 AM; it was also associated with lower regularity of weekday circadian rhythms based on steps and estimated with nonparametric measures of interdaily stability and autocorrelation as well as fewer steps-based daily peaks. Despite several reliable associations, our evidence showed limited ability of digital biomarkers to detect depression in the whole sample of working adults. However, in balanced and contrasted subsamples comprised of depressed and healthy participants with no risk of depression (ie, no or minimal depressive symptoms), the model achieved an accuracy of 80%, a sensitivity of 82%, and a specificity of 78% in detecting subjects at high risk of depression.

Conclusions

Digital biomarkers that have been discovered and are based on behavioral and physiological data from consumer wearables could detect increased risk of depression and have the potential to assist in depression screening, yet current evidence shows limited predictive ability. Machine learning models combining these digital biomarkers could discriminate between individuals with a high risk of depression and individuals with no risk.

Satisfaction, Usability, and Compliance With the Use of Smartwatches for Ecological Momentary Assessment of Knee Osteoarthritis Symptoms in Older Adults: Usability Study

Background

Smartwatches enable physicians to monitor symptoms in patients with knee osteoarthritis, their behavior, and their environment. Older adults experience fluctuations in their pain and related symptoms (mood, fatigue, and sleep quality) that smartwatches are ideally suited to capture remotely in a convenient manner.

Objective

The aim of this study was to evaluate satisfaction, usability, and compliance using the real-time, online assessment and mobility monitoring (ROAMM) mobile app designed for smartwatches for individuals with knee osteoarthritis.

Methods

Participants (N=28; mean age 73.2, SD 5.5 years; 70% female) with reported knee osteoarthritis were asked to wear a smartwatch with the ROAMM app installed. They were prompted to report their prior night’s sleep quality in the morning, followed by ecological momentary assessments (EMAs) of their pain, fatigue, mood, and activity in the morning, afternoon, and evening. Satisfaction, comfort, and usability were evaluated using a standardized questionnaire. Compliance with regard to answering EMAs was calculated after excluding time when the watch was not being worn for technical reasons (eg, while charging).

Results

A majority of participants reported that the text displayed was large enough to read (22/26, 85%), and all participants found it easy to enter ratings using the smartwatch. Approximately half of the participants found the smartwatch to be comfortable (14/26, 54%) and would consider wearing it as their personal watch (11/24, 46%). Most participants were satisfied with its battery charging system (20/26, 77%). A majority of participants (19/26, 73%) expressed their willingness to use the ROAMM app for a 1-year research study. The overall EMA compliance rate was 83% (2505/3036 responses). The compliance rate was lower among those not regularly wearing a wristwatch (10/26, 88% vs 16/26, 71%) and among those who found the text too small to read (4/26, 86% vs 22/26, 60%).

Conclusions

Older adults with knee osteoarthritis positively rated the ROAMM smartwatch app and were generally satisfied with the device. The high compliance rates coupled with the willingness to participate in a long-term study suggest that the ROAMM app is a viable approach to remotely collecting health symptoms and behaviors for both research and clinical endeavors.

Smart watch-detected tachycardia: a case of atrial flutter

The use of smart watches like the Apple watch and other wearable electronic devices by the general public has been increasing dramatically. Until their accuracy for detecting dysrhythmias has been well-established, however, it would not be appropriate to rely on them solely to rule-in or rule-out pathology. Nonetheless, unusual findings from these devices should be followed up with more conventional investigations, and this approach may prove highly beneficial to patients and treating clinicians alike. This case demonstrates a diagnosis of atrial flutter that was suspected based on the Apple watch and iPhone Health app findings.

Consumer Wearable Deployments in Actigraphy Research: Evaluation of an Observational Study

BACKGROUND

Consumer wearables can provide a practical and accessible method of data collection in actigraphy research. However, as this area continues to grow, it is becoming increasingly important for researchers to be aware of the many challenges facing the capture of quality data using consumer wearables.

OBJECTIVE

This study aimed to (1) present the challenges encountered by a research team in actigraphy data collection using a consumer wearable and (2) present considerations for researchers to apply in the pursuit of robust data using this approach.

METHODS

The Nokia Go was deployed to 33 elite Gaelic footballers from a single team for a planned period of 14 weeks. A bring-your-own-device model was employed for this study where the Health Mate app was downloaded on participants' personal mobile phones and connected to the Nokia Go via Bluetooth. Retrospective evaluation of the researcher and participant experience was conducted through transactional data such as study logs and email correspondence. The participant experience of the data collection process was further explored through the design of a 34-question survey utilizing aspects of the Technology Acceptance Model.

RESULTS

Researcher challenges included device disconnection, logistics and monitoring, and rectifying of technical issues. Participant challenges included device syncing, loss of the device, and wear issues, particularly during contact sport. Following disconnection issues, the data collection period was defined as 87 days for which there were 18 remaining participants. Average wear time was 79 out of 87 days (90%) and 20.8 hours per day. The participant survey found mainly positive results regarding device comfort, perceived ease of use, and perceived usefulness.

CONCLUSIONS

Although this study did not encounter some of the common published barriers to wearable data collection, our experience was impacted by technical issues such as disconnection and syncing challenges, practical considerations such as loss of the device, issues with personal mobile phones in the bring-your-own-device model, and the logistics and resources required to ensure a smooth data collection with an active cohort. Recommendations for achieving high-quality data are made for readers to consider in the deployment of consumer wearables in research.

Using Spontaneous Eye-blink Rates to Predict the Motor Status of Patients with Parkinson's Disease

Objective Assessing daily motor fluctuations is an important part of the disease management for patients with Parkinson's disease (PD). However, the frequent recording of subjective and/or objective assessments is not always feasible, and easier monitoring methods have been sought. Previous studies have reported that the spontaneous eye-blink rate (EBR) is correlated with the dopamine levels in the brain. Thus, the continuous monitoring of the EBR may be useful for predicting the motor status in patients with PD. Methods Electrooculograms (EOGs) were recorded for up to 7.5 hours from three PD patients using a wearable device that resembled ordinary glasses. An receiver operating characteristic (ROC) analysis was performed to compare the ability of the EBR estimates at each time-point (Blink Index) and the plasma levodopa levels to predict the motor status. Results The Blink Index was correlated with the plasma levodopa levels. When an indicator for the first hour of the observation period was included in the model, the Blink Index discerned wearing-off and dyskinesia as accurately as the plasma levodopa level. Conclusion Our study provides preliminary evidence regarding the utility of continuous EBR monitoring for the non-invasive evaluation of the motor status in patients with PD.

Acute Effect of Alcohol Intake on Cardiovascular Autonomic Regulation During the First Hours of Sleep in a Large Real-World Sample of Finnish Employees: Observational Study

BACKGROUND

Sleep is fundamental for good health, and poor sleep has been associated with negative health outcomes. Alcohol consumption is a universal health behavior associated with poor sleep. In controlled laboratory studies, alcohol intake has been shown to alter physiology and disturb sleep homeostasis and architecture. The association between acute alcohol intake and physiological changes has not yet been studied in noncontrolled real-world settings.

OBJECTIVE

The aim of this study was to assess the effects of alcohol intake on the autonomic nervous system (ANS) during sleep in a large noncontrolled sample of Finnish employees.

METHODS

From a larger cohort, this study included 4098 subjects (55.81%, 2287/4098 females; mean age 45.1 years) who had continuous beat-to-beat R-R interval recordings of good quality for at least 1 day with and for at least 1 day without alcohol intake. The participants underwent continuous beat-to-beat R-R interval recording during their normal everyday life and self-reported their alcohol intake as doses for each day. Heart rate (HR), HR variability (HRV), and HRV-derived indices of physiological state from the first 3 hours of sleep were used as outcomes. Within-subject analyses were conducted in a repeated measures manner by studying the differences in the outcomes between each participant's days with and without alcohol intake. For repeated measures two-way analysis of variance, the participants were divided into three groups: low (≤0.25 g/kg), moderate (>0.25-0.75 g/kg), and high (>0.75 g/kg) intake of pure alcohol. Moreover, linear models studied the differences in outcomes with respect to the amount of alcohol intake and the participant's background parameters (age; gender; body mass index, BMI; physical activity, PA; and baseline sleep HR).

RESULTS

Alcohol intake was dose-dependently associated with increased sympathetic regulation, decreased parasympathetic regulation, and insufficient recovery. In addition to moderate and high alcohol doses, the intraindividual effects of alcohol intake on the ANS regulation were observed also with low alcohol intake (all P<.001). For example, HRV-derived physiological recovery state decreased on average by 9.3, 24.0, and 39.2 percentage units with low, moderate, and high alcohol intake, respectively. The effects of alcohol in suppressing recovery were similar for both genders and for physically active and sedentary subjects but stronger among young than older subjects and for participants with lower baseline sleep HR than with higher baseline sleep HR.

CONCLUSIONS

Alcohol intake disturbs cardiovascular relaxation during sleep in a dose-dependent manner in both genders. Regular PA or young age do not protect from these effects of alcohol. In health promotion, wearable HR monitoring and HRV-based analysis of recovery might be used to demonstrate the effects of alcohol on sleep on an individual level.

Spike Timing-Dependent Plasticity in the Long-Latency Stretch Reflex Following Paired Stimulation from a Wearable Electronic Device

The long-latency stretch reflex (LLSR) in human elbow muscles probably depends on multiple pathways; one possible contributor is the reticulospinal tract. Here we attempted to induce plastic changes in the LLSR by pairing noninvasive stimuli that are known to activate reticulospinal pathways, at timings predicted to cause spike timing-dependent plasticity in the brainstem. In healthy human subjects, reflex responses in flexor muscles were recorded following extension perturbations at the elbow. Subjects were then fitted with a portable device that delivered auditory click stimuli through an earpiece, and electrical stimuli around motor threshold to the biceps muscle via surface electrodes. We tested the following four paradigms: biceps stimulus 10 ms before click (Bi-10ms-C); click 25 ms before biceps (C-25ms-Bi); click alone (C only); and biceps alone (Bi only). The average stimulus rate was 0.67 Hz. Subjects left the laboratory wearing the device and performed normal daily activities. Approximately 7 h later, they returned, and stretch reflexes were remeasured. The LLSR was significantly enhanced in the biceps muscle (on average by 49%) after the Bi-10ms-C paradigm, but was suppressed for C-25ms-Bi (by 31%); it was unchanged for Bi only and C only. No paradigm induced LLSR changes in the unstimulated brachioradialis muscle. Although we cannot exclude contributions from spinal or cortical pathways, our results are consistent with spike timing-dependent plasticity in reticulospinal circuits, specific to the stimulated muscle. This is the first demonstration that the LLSR can be modified via paired-pulse methods, and may open up new possibilities in motor systems neuroscience and rehabilitation.

SIGNIFICANCE STATEMENT

This report is the first demonstration that the long-latency stretch reflex can be modified by repeated, precisely timed pairing of stimuli known to activate brainstem pathways. Furthermore, pairing was achieved with a portable electronic device capable of delivering many more stimulus repetitions than conventional laboratory studies. Our findings open up new possibilities for basic research into these underinvestigated pathways, which are important for motor control in healthy individuals. They may also lead to paradigms capable of enhancing rehabilitation in patients recovering from damage, such as after stroke or spinal cord injury.

Design and Implementation of Foot-Mounted Inertial Sensor Based Wearable Electronic Device for Game Play Application

Wearable electronic devices have experienced increasing development with the advances in the semiconductor industry and have received more attention during the last decades. This paper presents the development and implementation of a novel inertial sensor-based foot-mounted wearable electronic device for a brand new application: game playing. The main objective of the introduced system is to monitor and identify the human foot stepping direction in real time, and coordinate these motions to control the player operation in games. This proposed system extends the utilized field of currently available wearable devices and introduces a convenient and portable medium to perform exercise in a more compelling way in the near future. This paper provides an overview of the previously-developed system platforms, introduces the main idea behind this novel application, and describes the implemented human foot moving direction identification algorithm. Practical experiment results demonstrate that the proposed system is capable of recognizing five foot motions, jump, step left, step right, step forward, and step backward, and has achieved an over 97% accuracy performance for different users. The functionality of the system for real-time application has also been verified through the practical experiments.