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Brain health attracts more recent attention as the population ages. Smartphone-based gait sensing and analysis can help identify the risks of brain diseases in daily life for prevention. Existing gait analysis approaches mainly hand-craft temporal gait features or developing CNN-based feature extractors, but they are either prone to lose some inconspicuous pathological information or are only dedicated to a single brain disease screening. We discover that the relationship between gait segments can be used as a principle and generic indicator to quantify multiple pathological patterns. In this paper, we propose NeuralGait, a pervasive smartphone-cloud system that passively captures and analyzes principle gait segments relationship for brain health assessment. On the smartphone end, inertial gait data are collected while putting the smartphone in the pants pocket. We then craft local temporal-frequent gait domain features and develop a self-attention-based gait segment relationship encoder. Afterward, the domain features and relation features are fed to a scalable RiskNet in the cloud for brain health assessment. We also design a pathological hot update protocol to efficiently add new brain diseases in the RiskNet. NeuralGait is practical as it provides brain health assessment with no burden in daily life. In the experiment, we recruit 988 healthy people and 417 patients with a single or combination of PD, TBI, and stroke, and evaluate the brain health assessment using a set of specifically designed metrics including global accuracy, exact accuracy, sensitivity, and false alarm rate. We also demonstrate the generalization (e.g., analysis of feature effectiveness and model efficiency) and inclusiveness of NeuralGait.

Slippery road conditions, such as snowy, icy or slushy pavements, are one of the major threats to road safety in winter. The U.S. Department of Transportation (USDOT) spends over 20% of its maintenance budget on pavement maintenance in winter. However, despite extensive research, it remains a challenging task to monitor pavement conditions and detect slippery roadways in real time. Most existing studies have mainly explored indirect estimates based on pavement images and weather forecasts. The emerging connected vehicle (CV) technology offers the opportunity to map slippery road conditions in real time. This study proposes a CV-based slippery detection system that uses vehicles to acquire data and implements deep learning algorithms to predict pavements' slippery conditions. The system classifies pavement conditions into three major categories: dry, snowy and icy. Different pavement conditions reflect different levels of slipperiness: dry surface corresponds to the least slippery condition, and icy surface to the most slippery condition. In practice, more attention should be paid to the detected icy and snowy pavements when driving or implementing pavement maintenance and road operation in winter. The classification algorithm adopted in this study is Long Short-Term Memory (LSTM), which is an artificial Recurrent Neural Network (RNN). The LSTM model is trained with simulated CV data in VISSIM and optimized with a Bayesian algorithm. The system can achieve 100%, 99.06% and 98.02% prediction accuracy for dry pavement, snowy pavement and icy pavement, respectively. In addition, it is observed that potential accidents can be reduced by more than 90% if CVs can adjust their driving speed and maintain a greater distance from the leading vehicle after receiving a warning signal. Simulation results indicate that the proposed slippery detection system and the information sharing function based on the CV technology and deep learning algorithm (i.e., the LSTM network implemented in this study) are expected to deliver real-time detection of slippery pavement conditions, thus significantly eliminating the potential risk of accidents.

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.

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 two weeks of NRT. Outcomes included acceptability (intervention evaluation and use), NRT utilization, 24-hour 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). However, Path2Quit resulted in significantly greater carbon monoxide confirmed ppa (AOR=3.55; 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 longer-term follow-up is warranted.

Using wireless signals to monitor human vital signs, especially heartbeat information, has been intensively studied in the past decade. This non-contact sensing modality can drive various applications from cardiac health, sleep, and emotion management. Under the circumstance of the COVID-19 pandemic, non-contact heart monitoring receives increasingly market demands. However, existing wireless heart monitoring schemes can only detect limited heart activities, such as heart rate, fiducial points, and Seismocardiography (SCG)-like information. In this paper, we present CardiacWave to enable a non-contact high-definition heart monitoring. CardiacWave can provide a full spectrum of Electrocardiogram (ECG)-like heart activities, including the details of P-wave, T-wave, and QRS complex. Specifically, CardiacWave is built upon the Cardiac-mmWave scattering effect (CaSE), which is a variable frequency response of the cardiac electromagnetic field under the mmWave interrogation. The CardiacWave design consists of a noise-resistant sensing scheme to interrogate CaSE and a cardiac activity profiling module for extracting cardiac electrical activities from the interrogation response. Our experiments show that the CardiacWave-induced ECG measures have a high positive correlation with the heart activity ground truth (i.e., measurements from a medical-grade instrument). The timing difference of P-waves, T-waves, and QRS complex is 0.67%, 0.71%, and 0.49%, respectively, and a mean cardiac event difference is within a delay of 5.3 milliseconds. These results indicate that CaridacWave offers high-fidelity and integral heart clinical characteristics. Furthermore, we evaluate the CardiacWave system with participants under various conditions, including heart and breath rates, ages, and heart habits (e.g., tobacco use).

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%).

Deep learning has been becoming a promising focus in data mining research. With deep learning techniques, researchers can discover deep properties and features of events from quantitative mobile sensor data. However, many data sources are geographically separated and have strict privacy, security, and regulatory constraints. Upon releasing the privacy-sensitive data, these data sources generally no longer physically possess their data and cannot interfere with the way their personal data being used. Therefore, it is necessary to explore distributed data mining architecture which is able to conduct consensus learning based on needs. Accordingly, we propose a distributed deep learning optimized system which contains a cloud server and multiple smartphone devices with computation capabilities and each device is served as a personal mobile data hub for enabling mobile computing while preserving data privacy. The proposed system keeps the private data locally in smartphones, shares trained parameters, and builds a global consensus model. The feasibility and usability of the proposed system are evaluated by three experiments and related discussion. The experimental results show that the proposed distributed deep learning system can reconstruct the behavior of centralized training. We also measure the cumulative network traffic in different scenarios and show that the partial parameter sharing strategy does not only preserve the performance of the trained model but also can reduce network traffic. User data privacy is protected on two levels. First, local private training data do not need to be shared with other people and the user has full control of their personal training data all the time. Second, only a small fraction of trained gradients of the local model are selected for sharing, which further reduces the risk of information leaking.

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.

Carolyn H Still,1 Seunghee Margevicius,2 Carla Harwell,3,4 Ming-Chun Huang,5 LaTonya Martin,6 Phuong B Dang,1 Jackson T Wright Jnr3,7 1Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, OH, USA; 2Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA; 3Department of Internal Medicine, School of Medicine, Case Western Reserve University, Cleveland, OH, USA; 4University Hospitals Cleveland Medical Center, Cleveland, OH, USA; 5Department of Electrical Engineering and Computer Science, Case School of Engineering, Case Western Reserve University, Cleveland, OH, USA; 6Community Partners, Cleveland Council of Black Nurses, Cleveland, OH, USA; 7Department of Nephrology and Hypertension, Clinical Hypertension Program, University Hospitals Cleveland Medical Center, Cleveland, OH, USACorrespondence: Carolyn H StillFrances Payne Bolton School of Nursing, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USATel +1 216 368 6338Fax +1 216 368 3452Email cwh11@case.eduPurpose: 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.Clinicaltrials.gov Identifier: NCT03722667.Keywords: African Americans, hypertension, minority health, technology, self-management

With the development of technologies, an increasing number of wearable devices that are currently at the heart of the development of the Internet of Things are used around the world. The concerns about privacy, in particular healthcare wearable devices, are exacerbated as the requirement for self-health monitoring increases. In addition, many sources of data are geographically separated and might not be allowed to release due to patients or regulatory constraints. Accordingly, in this paper, a distributed hierarchical deep learning system is proposed. The proposed system applying a distributed hierarchical neural network over a cloud server and smartphones. The system enables multiple smartphones to train a shared consensus model collaboratively while keeping the private data locally to protect data privacy, and the system takes advantage of the abundant computational resources on the cloud server to lower the computational overhead on smartphones. The proposed system is demonstrated by a fall detection study which is the common healthcare issue among human beings. The patients’ data are collected from multiple wearable devices including the smartphone, the smartwatch, and the smart insoles. The experimental results show that the distributed hierarchical deep learning system can reproduce the accuracy, specificity, precision, and sensitivity of centralized machine learning while preserving privacy.

In this paper, a system for lower-limb amputees to collect and analyze their locomotion activities is developed. Wearable Gait Lab (WGL) system is a pair of smart insoles that can collect plantar pressure data and foot motion data of subjects. A reinforcement learning model is trained to imitate the walking pattern of the lower-limb amputee on a musculoskeletal model by introducing realistic velocity data into the training process. The plantar pressure data collected from our Wearable Gait Lab were used to recognize the locomotion modes of the amputees. Experiments showed that the outcome of the musculoskeletal model can be a reference for analyzing muscle activities of the amputee. The system also shows the promising and stable performance of recognizing locomotion modes in amputees’ daily life. The accuracy of locomotion mode recognition reaches 98.02%. Monitoring muscle activities and locomotion modes of lower-limb amputees can help them prevent secondary impairments in rehabilitation.

Pain evaluation methods can represent pain information clearly yet inefficiently; they also cannot effectively display long-term pain records for review or comparison. They often must be supplemented by multidimensional scales with descriptors and labeled body figures. Pain is complicated to describe accurately, has a variety of causes, is related to pathology, and, in pain evaluation, needs improvements in information loss or incompleteness, dynamic pattern, long-term history tracking, and complexity and convenience of recording and maintaining this information. We propose a novel pain recording and tracking system using augmented reality (AR) that is complete and direct to use. Results show this app is reliable and appeals to users.

With the development of the Internet of Things (IoT), wearable technologies have been proposed to measure gait parameters in everyday life. However, since both diseases and activities could influence gait patterns, clinicians cannot use the measured gait parameters for clinical applications without knowing the corresponding activities. To address this problem, a novel gait analysis method—“gait analysis in terms of activities of daily living (ADLs)”—was proposed based on a wearable Smart Insole system. Twenty six gait parameters were extracted to realize a systematic gait analysis. Novel activity recognition algorithms based on characteristics of human gait were proposed to recognize ADLs, including “sitting,” “standing,” “walking,” “running,” “ascend stairs,” and “descend stairs” with high accuracy and low computation load. To evaluate the performance of “gait analysis in terms of ADLs,” an experiment consisting of a sequence of different ADLs was designed to simulate the scenario of everyday life. In the result, gait parameters measured during different activities were automatically highlighted with different colors, which made it easy to see whether the gait pattern change was caused by activities or diseases. Besides, a refined gait analysis could be realized by individually extracting and analyzing the gait parameters of a specific activity. The results indicate that “gait analysis in terms of ADLs” is a feasible method to reach the aim of bringing gait lab to everyday life.