Development and evaluation of visualizations of smoking data for integration into the Sense2Quit app for tobacco cessation.

IMPORTANCE: Due to insufficient smoking cessation apps for persons living with HIV, our study focused on designing and testing the Sense2Quit app, a patient-facing mHealth tool which integrated visualizations of patient information, specifically smoking use. OBJECTIVES: The purpose of this paper is to detail rigorous human-centered design methods to develop and refine visualizations of smoking data and the contents and user interface of the Sense2Quit app. The Sense2Quit app was created to support tobacco cessation and relapse prevention for people living with HIV. MATERIALS AND METHODS: Twenty people living with HIV who are current or former smokers and 5 informaticians trained in human-computer interaction participated in 5 rounds of usability testing. Participants tested the Sense2Quit app with use cases and provided feedback and then completed a survey. RESULTS: Visualization of smoking behaviors was refined through each round of usability testing. Further, additional features such as daily tips, games, and a homescreen were added to improve the usability of the app. A total of 66 changes were made to the Sense2Quit app based on end-user and expert recommendations. DISCUSSION: While many themes overlapped between usability testing with end-users and heuristic evaluations, there were also discrepancies. End-users and experts approached the app evaluation from different perspectives which ultimately allowed us to fill knowledge gaps and make improvements to the app. CONCLUSION: Findings from our study illustrate the best practices for usability testing for development and refinement of an mHealth-delivered consumer informatics tool for improving tobacco cessation yet further research is needed to fully evaluate how tools informed by target user needs improve health outcomes.

Pilot Testing of an mHealth App for Tobacco Cessation in People Living With HIV: Protocol for a Pilot Randomized Controlled Trial.

BACKGROUND: An estimated 40% of people living with HIV smoke cigarettes. Although smoking rates in the United States have been declining in recent years, people living with HIV continue to smoke cigarettes at twice the rate of the general population. Mobile health (mHealth) technology is an effective tool for people living with a chronic illness, such as HIV, as currently 84% of households in the United States report that they have a smartphone. Although many studies have used mHealth interventions for smoking cessation, few studies have recruited people living with HIV who smoke. OBJECTIVE: The objective of the pilot randomized controlled trial (RCT) is to examine the feasibility, acceptability, and preliminary efficacy of the Sense2Quit App as a tool for people living with HIV who are motivated to quit smoking. METHODS: The Sense2Quit study is a 2-arm RCT for people living with HIV who smoke cigarettes (n=60). Participants are randomized to either the active intervention condition, which consists of an 8-week supply of nicotine replacement therapy, standard smoking cessation counseling, and access to the Sense2Quit mobile app and smartwatch, or the control condition, which consists of standard smoking cessation counseling and a referral to the New York State Smokers' Quitline. The Sense2Quit app is a mobile app connected through Bluetooth to a smartwatch that tracks smoking gestures and distinguishes them from other everyday hand movements. In the Sense2Quit app, participants can view their smoking trends, which are recorded through their use of the smartwatch, including how often or how much they smoke and the amount of money that they are spending on cigarettes, watch videos with quitting tips, information, and distractions, play games, set reminders, and communicate with a study team member. RESULTS: Enrollment of study participants began in March 2023 and is expected to end in October 2023. All data collection is expected to be completed by the end of January 2024. This RCT will test the difference in outcomes between the control and intervention arms. The primary outcome will be the percentage of participants with biochemically verified 7-day point prevalence smoking or tobacco abstinence at their 12-week follow-up. Results from this pilot study will be disseminated to the research community following the completion of all data collection. CONCLUSIONS: The Sense2Quit study leverages mHealth so that it can help smokers improve their efforts at smoking cessation. Our research has the potential to not only increase quitting rates among people living with HIV who may need a prolonged, tailored intervention but also inform further development of mHealth for people living with HIV. This mHealth study will contribute significant findings to the greater mHealth research community, providing evidence as to how mHealth should be developed and tested among the target population. TRIAL REGISTRATION: ClinicalTrials.gov NCT05609032; https://clinicaltrials.gov/study/NCT05609032. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/49558.

Theoretically Guided Iterative Design of the Sense2Quit App for Tobacco Cessation in Persons Living with HIV.

The use of mobile health (mHealth technology) can be an effective intervention when considering chronic illnesses. Qualitative research methods were used to identify specific content and features for a mobile app for smoking cessation amongst people living with HIV (PWH). We conducted five focus group sessions followed by two Design Sessions with PWH who were or are currently chronic cigarette smokers. The first five groups focused on the perceived barriers and facilitators to smoking cessation amongst PWH. The two Design Sessions leveraged the findings from the focus group sessions and were used to determine the optimal features and user interface of a mobile app to support smoking cessation amongst PWH. Thematic analysis was conducted using the Health Belief Model and Fogg's Functional Triad. Seven themes emerged from our focus group sessions: history of smoking, triggers, consequences of quitting smoking, motivation to quit, messages to help quit, quitting strategies, and mental health-related challenges. Functional details of the app were identified during the Design Sessions and used to build a functional prototype.

An explainable COVID-19 detection system based on human sounds.

Acoustic signals generated by the human body have often been used as biomarkers to diagnose and monitor diseases. As the pathogenesis of COVID-19 indicates impairments in the respiratory system, digital acoustic biomarkers of COVID-19 are under investigation. In this paper, we explore an accurate and explainable COVID-19 diagnosis approach based on human speech, cough, and breath data using the power of machine learning. We first analyze our design space considerations from the data aspect and model aspect. Then, we perform data augmentation, Mel-spectrogram transformation, and develop a deep residual architecture-based model for prediction. Experimental results show that our system outperforms the baseline, with the ROC-AUC result increased by 5.47%. Finally, we perform an interpretation analysis based on the visualization of the activation map to further validate the model.

Smoking Cessation System for Preemptive Smoking Detection.

Smoking cessation is a significant challenge for many people addicted to cigarettes and tobacco. Mobile health-related research into smoking cessation is primarily focused on mobile phone data collection either using self-reporting or sensor monitoring techniques. In the past 5 years with the increased popularity of smartwatch devices, research has been conducted to predict smoking movements associated with smoking behaviors based on accelerometer data analyzed from the internal sensors in a user's smartwatch. Previous smoking detection methods focused on classifying current user smoking behavior. For many users who are trying to quit smoking, this form of detection may be insufficient as the user has already relapsed. In this paper, we present a smoking cessation system utilizing a smartwatch and finger sensor that is capable of detecting pre-smoking activities to discourage users from future smoking behavior. Pre-smoking activities include grabbing a pack of cigarettes or lighting a cigarette and these activities are often immediately succeeded by smoking. Therefore, through accurate detection of pre-smoking activities, we can alert the user before they have relapsed. Our smoking cessation system combines data from a smartwatch for gross accelerometer and gyroscope information and a wearable finger sensor for detailed finger bend-angle information. We compare the results of a smartwatch-only system with a combined smartwatch and finger sensor system to illustrate the accuracy of each system. The combined smartwatch and finger sensor system performed at an 80.6% accuracy for the classification of pre-smoking activities compared to 47.0% accuracy of the smartwatch-only system.

A Smoking Cessation Mobile App for Persons Living With HIV: Preliminary Efficacy and Feasibility Study.

BACKGROUND: The prevalence of smoking in the United States general population has gradually declined to the lowest rate ever recorded; however, this has not been true for persons with HIV. OBJECTIVE: We conducted a pilot test to assess the feasibility and efficacy of the Lumme Quit Smoking mobile app and smartwatch combination with sensing capabilities to improve smoking cessation in persons with HIV. METHODS: A total of 40 participants were enrolled in the study and randomly assigned 1:1 to the control arm, which received an 8-week supply of nicotine replacement therapy, a 30-minute smoking cessation counseling session, and weekly check-in calls with study staff, or to the intervention arm, which additionally received the Lumme Quit Smoking app and smartwatch. RESULTS: Of the 40 participants enrolled, 37 completed the follow-up study assessments and 16 used the app every day during the 56-day period. During the 6-month recruitment and enrollment period, 122 people were screened for eligibility, with 67.2% (82/122) deemed ineligible. Smoking criteria and incompatible tech were the major reasons for ineligibility. There was no difference in the proportion of 7-day point prevalence abstinence by study arm and no significant decrease in exhaled carbon monoxide for the intervention and control arms separately. However, the average exhaled carbon monoxide decreased over time when analyzing both arms together (P=.02). CONCLUSIONS: Results suggest excellent feasibility and acceptability of using a smoking sensor app among this smoking population. The knowledge gained from this research will enable the scientific community, clinicians, and community stakeholders to improve tobacco cessation outcomes for persons with HIV. TRIAL REGISTRATION: ClinicalTrials.gov NCT04808609; https://clinicaltrials.gov/ct2/show/NCT04808609.

A fitness training optimization system based on heart rate prediction under different activities.

Heart rate can be considered as an indicator of the exercise intensity in people's daily physical activities. Five heart rate zone theory is commonly adopted by individuals and professional athletes during their exercises and training. These heart rate zones are based upon percentages of people's maximal heart rate, which indicate different exercise intensities. The aim of paper is to propose an optimization training system based on dynamic heart rate prediction, which can predict people's heart rate under three different types of exercises: walking, running and rope jumping. The system can help people optimize their exercise by advising them to adjust the speed or workload to reach their predetermined training intensity under different activities. Four Long Short-Term Memory (LSTM) neural networks are deployed, one for human activity recognition (HAR) and three for heart rate prediction.

Randomized controlled trial testing a video-text tobacco cessation intervention among economically disadvantaged African American adults.

OBJECTIVE: This pilot study tested the acceptability and short-term outcomes of a culturally specific mobile health (mHealth) intervention (Path2Quit) in a sample of economically disadvantaged African American adults. We hypothesized that Path2Quit would demonstrate greater acceptability, biochemically verified abstinence, and promote nicotine replacement therapy (NRT) use compared with a standard text-messaging program. METHOD: In a 2-arm pilot randomized trial, adults who sought to quit smoking (N = 119) received either Path2Quit or the National Cancer Institute's (NCI) SmokefreeTXT, both combined with a brief behavioral counseling session plus 2 weeks of NRT. Outcomes included acceptability (intervention evaluation and use), NRT utilization, 24-hr quit attempts, self-reported 7-day point prevalence abstinence (ppa), and biochemically verified smoking abstinence at the 6-week follow-up. RESULTS: Participants were 52% female/48% male, mostly single (60%), completed ≥ 12 years of education (83%), middle-aged, and 63% reported a household income < $10K/year. Participants smoked 11 (SD = 8.2) cigarettes/day for 25 (SD = 16) years, and reported low nicotine dependence. There were no differences in intervention evaluations or use (ps > .05), yet Path2Quit led to significantly greater NRT utilization at follow-up (p < .05). There was no difference in quit attempts between conditions or 7-day ppa (p > .05). However, Path2Quit resulted in significantly greater carbon monoxide confirmed ppa (adjusted odds ratio [AOR] = 3.55; 95% CI [1.32, 9.54]) at the 6-week follow-up. CONCLUSIONS: A culturally specific mHealth intervention demonstrated positive effects on NRT use and short-term abstinence. Additional research in a larger sample and with long-term follow-up is warranted. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

mHealth Technologies Towards Parkinson's Disease Detection and Monitoring in Daily Life: A Comprehensive Review.

Parkinson's disease (PD) can gradually affect people's lives thus attracting tremendous attention. Early PD detection and treatment can help control the disease progress, relief from the symptoms and improve the patients' life quality. However, the current practice of PD diagnosis is conducted in a clinical setup and administrated by a PD specialist due to the early signs of PD are not noticeable in daily life. According to the report of CDC/NIH, the diagnosed time of PD ranges from 2-10 years after onset. Therefore, a more accessible PD diagnosis approach is urgently demanded. In recent years, mobile health (for short mHealth) technology has been intensively investigated for preventive medicine, particularly in chronic disease management. Notably, many types of research have explored the possibility of using mobile and wearable personal devices to detect the symptom of PD and shown promising results. It provides opportunities for transforming early PD detection from clinical to daily life. This survey paper attempts to conduct a comprehensive review of mHealth technologies for PD detection from 2000 to 2019, and compares their pros and cons in practical applications and provides insights to close the performance gap between state-of-the-art clinical approaches and mHealth technologies.

Ubiquitous Fall Hazard Identification With Smart Insole.

Falls are leading causes of nonfatal injuries in workplaces which lead to substantial injury and economic consequences. To help avoid fall injuries, safety managers usually need to inspect working areas routinely. However, it is difficult for a limited number of safety managers to inspect fall hazards instantly especially in large workplaces. To address this problem, a novel fall hazard identification method is proposed in this paper which makes it possible for all workers to report the potential hazards automatically. This method is based on the fact that people use different gaits to get across different floor surfaces. Through analyzing gait patterns, potential fall hazards could be identified automatically. In this research, Smart Insole, an insole shaped wearable system for gait analysis, was applied to measure gait patterns for fall hazard identification. Slips and trips are the focus of this study since they are two main causes of falls in workplaces. Five effective gait features were extracted to train a Support Vector Machine (SVM) model for recognizing slip hazard, trip hazard, and safe floor surfaces. Experiment results showed that fall hazards could be recognized with high accuracy (98.1%).

A context-adaptive smoking cessation system using videos.

Cigarette smoking is the primary preventable cause of death and disease worldwide. Studies reveal that smoking is associated with psychiatric symptoms, sociodemographic characteristics, social stressors, and lack of social support. In general, smokers report poorer mental health and benefit from support to be able to quit smoking (Jorm et al., 1999). In this paper, a tailored smoking cessation system has been developed in which the counseling and support is delivered via video-messaging. The system engages users in adaptive motivating video access. Users can interact with the system and the system selects the best matching video for them by processing their messages using Natural Language Processing (NLP). We have tailored 77 videos for interactive contents that encompass important issues users might face during the process of smoking cessation. A novel application-based data driven approach has been taken for categorizing videos to push to participants. The approach is based on analyzing 750 messages of people in the cessation process. We observed that most of the messages' contents were about smoking health effects, cravings, triggers, relapse, positive mood, low cessation self efficacy, medications, and culturally specific targeting inquiries. Considering these categories, videos are categorized to the corresponding groups by an intelligent approach. The information underlying the data driven categories allows for improving and facilitating smoking status assessment. The system has the potential for improving future smoking cessation decision-making adaptive interventions and health monitoring systems. The goal is to tailor the system to meet the needs of the users in real-time and maximize the potential impact.

A Community and Technology-Based Approach for Hypertension Self-Management (COACHMAN) to Improve Blood Pressure Control in African Americans: Results from a Pilot Study.

PURPOSE: Adoption of technology has increased to support self-managing chronic diseases. However, behavioral interventions evaluating such technology have been understudied in African Americans with hypertension. The aim of this study was to explore a community and technology-based intervention for hypertension self-management (COACHMAN) intervention on blood pressure (BP) control and health-related quality of life (HRQoL) in African Americans with hypertension. METHODS: Sixty African Americans (mean age 60; 75% females) who were prescribed antihypertensive medications and owning a smartphone were randomized to the COACHMAN (n = 30) or enhanced usual care (n = 30) group for 12 weeks. COACHMAN is comprised of four components: web-based education, home BP monitoring, medication management application, and nurse counseling. Hypertension knowledge, self-efficacy, technology adoption/use, medication adherence, BP, and HRQoL scores were assessed. RESULTS: Mean systolic and diastolic BP at baseline was 150.49 (SD = 13.89) and 86.80 (SD = 13.39), respectively. After completing the 3-month intervention to improve hypertension self-management, the groups did not significantly differ in BP control and HRQoL. Clinically relevant BP reduction was observed in the intervention group. Paired t-test showed that mean medication-taking adherence scores significantly improved in the intervention group (P = 0.023) compared to the control group (P = 0.075). CONCLUSION: Using technology may have a positive impact on supporting hypertension self-management, particularly in medication-taking adherence. Further research is warranted in a larger sample and should include standardization of medication management to isolate the effects of behavioral interventions on changes in BP. CLINICALTRIALSGOV IDENTIFIER: NCT03722667.

Efficient mapping of crash risk at intersections with connected vehicle data and deep learning models.

Traditional methods for identifying crash-prone roadways are mainly based on historical crash data. It usually requires more than three years to collect a sufficient amount of dataset for road safety assessment. However, the emerging connected vehicles (CVs) technology generates rich instantaneous information, which can be used to identify dangerous road sections proactively. Information about the identified crash-prone intersections can be shared with the surrounding vehicles via CVs communication technology to promote cautious driving behaviors; in the longer term, such information will guide the implementation of countermeasures to prevent potential crashes. This study proposed a deep-learning based method to predict the risk level at intersections based on CVs data from the Michigan Safety Pilot program and historical traffic and intersection crash data in areas around Ann Arbor, Michigan, USA. One month of data by CVs at intersections were used for analyses, which accounts for about 3%-12% of overall trips. The risk levels of 774 intersections (i.e., low, medium and high risk) are determined by the annual crash rates. Feature extraction process is applied to both CV's data and traffic data at each intersection and 24 features are extracted. Two black-box deep-learning models, multi-layer perceptron (MLP) and convolutional neural network (CNN) are trained with the extracted features. A number of hyperparameters that affect prediction performance are fine-tuned using Bayesian optimization algorithm for each model. The performance of the two deep learning models, which are black-box models, were also compared with a decision tree model, a white-box type of simple machine learning model. The results showed that the accuracies of deep learning (DL) models were slightly better (both over 90 %) than the decision tree model (about 87 %). This indicated that the DL models were capable of uncover the inherent complexity from the dataset and therefore provided higher accuracy than the traditional machine learning model. CNN model achieves slightly higher accuracy (93.8 %) and is recommended as the classifier to predict the risk level at intersections in practice. The interpretability analysis of the CNN model is conducted to confirm the validity of the model. This study shows that combination of CVs data (V2V and V2I) and deep learning networks (i.e. MLP and CNN used in this paper) is promising to determine crash risks at intersections with high time efficiency and at low CV penetration rates, which help to deploy countermeasures to reduce the crash rates and resolve traffic safety problems.

Using Mobile Electroencephalography and Actigraphy to Diagnose Attention-Deficit/Hyperactivity Disorder: Case-Control Comparison Study.

BACKGROUND: Children with attention-deficit/hyperactivity disorder (ADHD), a neurobehavioral disorder, display behaviors of inattention, hyperactivity, or impulsivity, which can affect their ability to learn and establish proper family and social relationships. Various tools are currently used by child and adolescent psychiatric clinics to diagnose, evaluate, and collect information and data. The tools allow professional physicians to assess if patients need further treatment, following a thorough and careful clinical diagnosis process. OBJECTIVE: We aim to determine potential indicators extracted from a mobile electroencephalography (EEG) device (Mindset; NeuroSky) and an actigraph (MotionWatch 8; CamNtech) and to validate them for diagnosis of ADHD. The 3 indicators are (1) attention, measured by the EEG; (2) meditation, measured by the EEG; and (3) activity, measured by the actigraph. METHODS: A total of 63 participants were recruited. The case group comprised 40 boys and 9 girls, while the control group comprised 5 boys and 9 girls. The groups were age matched. The test was divided into 3 stages-pretest, in-test, and posttest-with a testing duration of 20 minutes each. We used correlation analysis, repeated measures analysis of variance, and regression analysis to investigate which indicators can be used for ADHD diagnosis. RESULTS: With the EEG indicators, the analysis results show a significant correlation of attention with both hit reaction time (RT) interstimulus interval (ISI) change (r=-0.368; P=.003) and hit standard error (SE) ISI change (r=-0.336; P=.007). This indicates that the higher the attention of the participants, the smaller both the hit RT change and the hit SE ISI change. With the actigraph indicator, confidence index (r=0.352; P=.005), omissions (r=0.322; P=.01), hit RT SE (r=0.393; P=.001), and variability (r=0.351; P=.005) were significant. This indicates that the higher the activity amounts, the higher the impulsive behavior of the participants and the more target omissions in the continuous performance test (CPT). The results show that the participants with ADHD present a significant difference in activity amounts (P<0.001). The actigraph outperforms the EEG in screening ADHD. CONCLUSIONS: When the participants with ADHD are stimulated under restricted conditions, they will present different amounts of activity than in unrestricted conditions due to participants' inability to exercise control over their concentration. This finding could be a new electronic physiological biomarker of ADHD. An actigraph can be used to detect the amount of activity exhibited and to help physicians diagnose the disorder in order to develop more objective, rapid auxiliary diagnostic tools.

Use of the Phase-Based Model of Smoking Treatment to Guide Intervention Development for Persons Living with HIV Who Self-Identify as African American Tobacco Smokers.

Cigarette smoking is highly prevalent among persons living with the human immunodeficiency virus (HIV) (PLWH), with rates as high 50% as compared to 14% in the general U.S. population. Tobacco use causes morbidity and mortality in PLWH, and tobacco-related harm is substantially higher in PLWH than smokers in the general population, providing the scientific premise for developing effective tobacco cessation interventions in this population. To better address this issue, we conducted six focus group sessions with 45 African American smokers who are living with HIV to understand the barriers to smoking cessation and the strategies that would be helpful to overcome these barriers. We organized our findings by the Phase-Based Model of Smoking Treatment to understand the intervention components that are needed at each phase to help PLWH successfully quit smoking. Participants in our focus group sessions articulated key components for incorporation into tobacco cessation intervention for PLWH: a personalized plan for quitting, reminders about that plan, and a support system. Participants thought that their HIV and tobacco use were disassociated. Participants described barriers to the use of pharmacotherapy, including adverse side effects of the gum and patch and concerns about the negative health effects of some oral medications. Substance use was identified as a commonly co-occurring condition as well as a barrier to successfully ceasing to smoke tobacco products. In summary, these findings offer information on the components of a tobacco cessation intervention for PLWH, namely reminders, a support system, substance use treatment, and monitoring to prevent relapse.

Body-Earth Mover's Distance: A Matching-Based Approach for Sleep Posture Recognition.

Sleep posture is a key component in sleep quality assessment and pressure ulcer prevention. Currently, body pressure analysis has been a popular method for sleep posture recognition. In this paper, a matching-based approach, Body-Earth Mover's Distance (BEMD), for sleep posture recognition is proposed. BEMD treats pressure images as weighted 2D shapes, and combines EMD and Euclidean distance for similarity measure. Compared with existing work, sleep posture recognition is achieved with posture similarity rather than multiple features for specific postures. A pilot study is performed with 14 persons for six different postures. The experimental results show that the proposed BEMD can achieve 91.21% accuracy, which outperforms the previous method with an improvement of 8.01%.

A Self-Calibrating Radar Sensor System for Measuring Vital Signs.

Vital signs (i.e., heartbeat and respiration) are crucial physiological signals that are useful in numerous medical applications. The process of measuring these signals should be simple, reliable, and comfortable for patients. In this paper, a noncontact self-calibrating vital signs monitoring system based on the Doppler radar is presented. The system hardware and software were designed with a four-tiered layer structure. To enable accurate vital signs measurement, baseband signals in the radar sensor were modeled and a framework for signal demodulation was proposed. Specifically, a signal model identification method was formulated into a quadratically constrained l1 minimization problem and solved using the upper bound and linear matrix inequality (LMI) relaxations. The performance of the proposed system was comprehensively evaluated using three experimental sets, and the results indicated that this system can be used to effectively measure human vital signs.

BreathSens: A Continuous On-Bed Respiratory Monitoring System With Torso Localization Using an Unobtrusive Pressure Sensing Array.

The ability to continuously monitor respiration rates of patients in homecare or in clinics is an important goal. Past research showed that monitoring patient breathing can lower the associated mortality rates for long-term bedridden patients. Nowadays, in-bed sensors consisting of pressure sensitive arrays are unobtrusive and are suitable for deployment in a wide range of settings. Such systems aim to extract respiratory signals from time-series pressure sequences. However, variance of movements, such as unpredictable extremities activities, affect the quality of the extracted respiratory signals. BreathSens, a high-density pressure sensing system made of e-Textile, profiles the underbody pressure distribution and localizes torso area based on the high-resolution pressure images. With a robust bodyparts localization algorithm, respiratory signals extracted from the localized torso area are insensitive to arbitrary extremities movements. In a study of 12 subjects, BreathSens demonstrated its respiratory monitoring capability with variations of sleep postures, locations, and commonly tilted clinical bed conditions.

Machine learning-based assessment tool for imbalance and vestibular dysfunction with virtual reality rehabilitation system.

BACKGROUND AND OBJECTIVE: Dizziness is a major consequence of imbalance and vestibular dysfunction. Compared to surgery and drug treatments, balance training is non-invasive and more desired. However, training exercises are usually tedious and the assessment tool is insufficient to diagnose patient's severity rapidly. METHODS: An interactive virtual reality (VR) game-based rehabilitation program that adopted Cawthorne-Cooksey exercises, and a sensor-based measuring system were introduced. To verify the therapeutic effect, a clinical experiment with 48 patients and 36 normal subjects was conducted. Quantified balance indices were measured and analyzed by statistical tools and a Support Vector Machine (SVM) classifier. RESULTS: In terms of balance indices, patients who completed the training process are progressed and the difference between normal subjects and patients is obvious. CONCLUSIONS: Further analysis by SVM classifier show that the accuracy of recognizing the differences between patients and normal subject is feasible, and these results can be used to evaluate patients' severity and make rapid assessment.

Using pressure map sequences for recognition of on bed rehabilitation exercises.

Physical rehabilitation is an important process for patients recovering after surgery. In this paper, we propose and develop a framework to monitor on-bed range of motion exercises that allows physical therapists to evaluate patient adherence to set exercise programs. Using a dense pressure sensitive bedsheet, a sequence of pressure maps are produced and analyzed using manifold learning techniques. We compare two methods, Local Linear Embedding and Isomap, to reduce the dimensionality of the pressure map data. Once the image sequences are converted into a low dimensional manifold, the manifolds can be compared to expected prior data for the rehabilitation exercises. Furthermore, a measure to compare the similarity of manifolds is presented along with experimental results for five on-bed rehabilitation exercises. The evaluation of this framework shows that exercise compliance can be tracked accurately according to prescribed treatment programs.