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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.

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%).

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.

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.

With the development of Internet of Things (IoT), indoor localization has been a research focus in recent years. For inertial measurement unit (IMU)-based indoor localization method, zero velocity update (ZUPT) uses the known velocity at stationary epoch as a benchmark to calibrate the velocity drift. However, stationary epoch only takes up 24% of a whole gait cycle time, and the velocity drift at the remaining 76% time is usually estimated according to an assumption that velocity has a linear drift over time, which would introduce errors. In this paper, a two-step velocity calibration method was proposed based on human gait characteristics with Smart Insole: known velocity update (KUPT) and double-foot position calibration (DFPC). KUPT could measure the velocity from heel-strike to toe-off based on the recorded real-time foot angle and the shoe dimensions, which increases the time period when the velocity could be measured from 24% to 62% of a whole gait cycle time. DFPC method could fuse the position information of both feet based on the symmetrical characteristic of human gait to further increase the reliability of the localization results. The statistical result of a 20 times 20-m walking experiment showed that KUPT method was more accurate and reliable than ZUPT method for both feet, and DFPC method could further improve the result of KUPT method. Another experiment about walking in an indoor environment for 91 m showed that the proposed KUPT+DFPC method had an error of about 0.78 m which is acceptable for most IoT applications.

Tobacco smoking is responsible for one out of every five deaths in the US, according to the Centers for Disease Control and Prevention (CDC). Recent advances in treatment delivery include technology-based mobile health approaches, which seek to deliver real-time feedback to smokers to aid quit attempts and mitigate lapses. With regard to the measurement of smoking, clinical trials rely on participant self-report and/or biochemical verification of smoking status to evaluate outcomes. Wearable sensors have the potential to improve current approaches by providing personalized feedback and objective verification of smoking status ( Burns, 2000 ). In this paper, we describe the development of a novel smoking cessation system that combines motion detection and an Android software application to monitor smoking in real-time. In this system, a personalized smoking cessation plan will be created based on the goal of complete cessation or smoking reduction. Once the plan is created, the mobile system will monitor the users׳ smoking activity and provide feedback. An LSTM algorithm has been computed to train and test the motion data, which was collected from two armbands, to detect smoking and non-smoking motions. The internet message service will be used to remind users to stick to their plan when the sensor detects current smoking. Related video links are pushed and pulled to the users via Short Message Service (SMS) to support smoking cessation. Findings have implications for tobacco cessation treatment delivery and assessment of smoking status.

Risk factors, such as overexertion, awkward postures, excessive repetition, and the combination of these factors are main causes of work-related musculoskeletal disorders (WMSDs). In this paper, we proposed an automatic WMSDs risk factors identification and visualization method based on Wearable and Connected Gait Analytics System (WCGAS) and Kinect skeleton models. WCGAS was capable of recording plantar pressure from which postures, force exertions, and repetitions could be recognized with algorithms such as sequential minimal optimization (SMO) algorithm and long short term memory (LSTM) network. Kinect skeleton models were used to make the WMSDs risk factors visualized. Experiments with quasi-static and sequential postures were designed to evaluate the recognition performance of work-related motion type (i.e. “lifting”, “carrying”, “bending”, “pulling”, and “pushing”). A load variable (with/without 10 kg load) was introduced for evaluating the performance of force exertions recognition. 5 repetitions of each motion were used for evaluating the performance of repetitions recognition. Results showed that quasi-static postures could be recognized with 100% accuracy and the accuracy for sequential motions recognition were 74%, 79%, 92%, 99% and 99% for “bending”, “carrying”, “lifting”, “pulling” and “pushing”, respectively. Force exertions were recognized with 100% accuracy. For repetitions recognition, except the accuracy in the “bending” motion was 80%, the repetitions of other motions could be recognized correctly. Kinect skeleton model showed its ability of making the WMSDs risk factors vivid which would contribute to the accuracy of WMSDs risks evaluation. These results indicated that it is possible to use WCGAS and Kinect skeleton models for WMSDs risk factors identification and visualization applications.

Acquiring nutritional information before buying and eating food is crucial for making healthier choices and more educated selections. Therefore, there is a demand for electronic devices that can automatically access nutrition information. To help with this, we propose a novel augmented reality (AR)-based wearable food-monitoring system to make food nutritional information available and visible in real-life scenarios, such as at the supermarket. It combines reverse image search (RIS) and text mining to recognize the food, and then it retrieves nutritional information from a remote database. For userfriendliness, the information is overlaid on a live feed from a front-facing camera.

Facial expression recognition plays an important role on mimicking and synchronizing person's mental activity. Various approaches have been employed for recognition on facial expression, like facial image and video analysis. Bio-signals as very important biological information reflect biological features. With the development of sensor technology and machine learning, collecting bio-signals to study facial expressions is practicable. Surface-Electromyogram is a way to collect EMG signal by sticking a signal collector on the surface of skin, much environment interference can be ignored and privacy can be protected. Inconvenience of collecting these kinds of bio-signal resulted in lacking of good publicly available datasets. In this paper, we have designed an facial expressions recognition system based on sEMG signals using Intel Edison board with advantages of high temporal resolution, potential flexibility of testing devices. The paper studies facial expressions comprehensively, abstracts 8 common types of facial expressions from 2 kinds of classes and establishes a new bio-EMG dataset with 1680 instances. The facial expressions are including three periodic expressions (chewing, speaking, gargling) and five transient expressions (sadness, surprise, happiness, pout, and angry). For recognition accuracy, by utilizing three different classifiers, Cubic SVM, Cubic KNN, and Gaussian SVM, each classifier has a good performance on expressions classification, the Cubic SVM classifier has the best performance of these three with accuracy as high as 99.52%. With the decreasing of sample amount for training model, the Cubic SVM classifier still performs well in classification with accuracy of 86.9%.

Sensory devices have the potential to improve monitoring balance tests and providing quantitative supports for activity analysis. The Wearable Gait Lab system is proposed to monitor activities of feet and legs during balance tests. In this system, a wearable underfoot force sensing unit is used to record foot motions and plantar pressure data; a joint angular and EMG sensing unit is used to record leg motions and muscular data; and an android application is implemented to control all units, monitor the data recording process, and upload recorded data to cloud server, which allows health professionals to review it remotely. The system provides adequate data for balance ability analysis and simplifies the scoring processes in balance tests. The proposed system is evaluated with standard balance tests (Limits of Stability, Sit-To-Stand, and Rhythmic Weight Shift), whereas the data collected are analyzed with data mining techniques to verify the reliability of the designated process. Certain parameters are computed such as Center of Gravity (COG), weight transfer time, and sway velocities. The results show that the system is informational and reliable in the process of determining balance status during tests with additional advantages in high portability and efficient review communications.

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.

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.

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.

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.

Sleep posture affects the quality of our sleep and is especially important for such medical conditions as sleep apnea and pressure ulcers. In this paper, we propose a design for a dense pressure-sensitive bedsheet along with an algorithmic framework to recognize and monitor sleeping posture. The bedsheet system uses comfortable textile sensors that produces high-resolution pressure maps. We develop a novel framework for pressure image analysis to monitor sleep postures, including a set of geometrical features for sleep posture characterization and three sparse classifiers for posture recognition. In demonstrating this system, we run 2 pilot studies: one evaluates the performance of our methods with 14 subjects to analyze 6 common postures; the other is a series of overnight studies to verify continuous performance. The experimental results show that our proposed method enables reliable sleep posture recognition and offers better overall performance than traditional methods, achieving up to 83.0% precision and 83.2% recall on average.

Detecting human activity independent of intensity is essential in many applications, primarily in calculating metabolic equivalent rates and extracting human context awareness. Many classifiers that train on an activity at a subset of intensity levels fail to recognize the same activity at other intensity levels. This demonstrates weakness in the underlying classification method. Training a classifier for an activity at every intensity level is also not practical. In this paper, we tackle a novel intensity-independent activity recognition problem where the class labels exhibit large variability, the data are of high dimensionality, and clustering algorithms are necessary. We propose a new robust stochastic approximation framework for enhanced classification of such data. Experiments are reported using two clustering techniques, K-Means and Gaussian Mixture Models. The stochastic approximation algorithm consistently outperforms other well-known classification schemes which validate the use of our proposed clustered data representation. We verify the motivation of our framework in two applications that benefit from intensity-independent activity recognition. The first application shows how our framework can be used to enhance energy expenditure calculations. The second application is a novel exergaming environment aimed at using games to reward physical activity performed throughout the day, to encourage a healthy lifestyle.