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Various technologies such as WiFi, Bluetooth, and RFID are being used to provide indoor location-based services (LBS). In particular, a WiFi base using a WiFi AP already installed in an indoor space is widely applied, and the importance of indoor location recognition using deep running has emerged. In this study, we propose a WiFi-based indoor location recognition system using a smart watch, which is extended from an existing smartphone. Unlike the existing system, we use both the Received Signal Strength Indication (RSSI) and Basic Service Set Identifier (BSSID) to solve the problem of position recognition owing to the similar signal strength. By performing two times of filtering, we want to improve the execution time and accuracy through the learning of random forest based location awareness. In an unopened indoor space with five or more WiFi APs installed. Experiments were conducted by comparing the results according to the number of data for supposed system and a system based on existing WiFi fingerprint based random forest. The proposed system was confirmed to exhibit high performance in terms of execution time and accuracy. It has significance in that the system shows a consistent performance regardless of the number of data for location information.

Attention-deficit hyperactivity disorder (ADHD) is primarily diagnosed using set criteria and checklists. However, authors have indicated that such criteria and checklists are subjective. In this study, data from the gyroscope and accelerometer in a smart watch were used to analyze the movements of children with ADHD. This study cohort comprised 15 children with ADHD and 15 age- and sex-matched control participants. The children with ADHD and controls wore the watches on their non-writing hands simultaneously in class. The recordings of one patient and one control were tracked for 2 h daily for three consecutive days with desk and seated class activities. We compared the measurements of variance and the zero-crossing rate (ZCR) of the gyroscope and accelerometer between the children with ADHD and controls. All average variance and ZCR values of the three axes (x, y, and z) in the gyroscope and accelerometer were higher in children with ADHD than in the controls. Significant differences in average variance values on the y-axis (p < 0.001) and ZCR values on all three axes (x, p = 0.005; y, p = 0.003; and z, p = 0.004) of the gyroscope were observed. Similarly, significant differences in the average variance values on the three axes (x, p = 0.001; y, p < 0.001; and z, p < 0.001) and ZCR values on the z-axis (p = 0.006) of the accelerometer were observed. The proposed method is a promising tool to objectively analyze the movements of children with ADHD at school.

The objective of this study was to determine the antecedents of continuance intention to use smart watch by integrating Technology Acceptance Model constructs with satisfaction and social influence. The data was collected from 159 respondents through the snowball sampling technique and was analyzed using Structural Equation Modeling. The results confirmed a significant impact of the following four factors: a) social influence, attitude towards smart watches and satisfaction on continuance intention; b) perceived usefulness and perceived ease of use on attitude; c) perceived usefulness on satisfaction; and d) perceived ease of use on perceived usefulness. The model is found to be having a moderate R (2) value, wherein, continuance intention to use smart watch has a 57.6% variance, attitude has a 61.5% variance, and satisfaction has a variance of 47.9%. The model also found the significant indirect effect of perceived ease of use on continuance intention, satisfaction, attitude; and perceived usefulness on continuance intention.

Combined therapy with tiotropium and olodaterol notably improves parameters of lung function and quality of life in patients with chronic obstructive pulmonary disease (COPD) compared to mono-components; however, its effect on physical activity is unknown. The present study evaluated whether combination therapy affects daily physical performance in patients with COPD under a smart watch-based encouragement program. This was a non-blinded clinical trial with no randomization or placebo control. A total of 20 patients with COPD were enrolled in the present study. The patients carried an accelerometer for 4 weeks; they received no therapy during the first 2 weeks but they were treated with combined tiotropium and olodaterol under a smart watch-based encouragement program for the last 2 weeks. The pulmonary function test, COPD assessment test, 6-min walk distance and parameters of physical activity were significantly improved (P<0.05) by combination therapy under smart watch-based coaching compared with values prior to treatment. To the best of our knowledge, the present study for the first time provides evidence that smart watch-based coaching in combination with tiotropium and olodaterol may improve daily physical activity in chronic obstructive pulmonary disease.

Purpose - The purpose of this paper is to identify the key psychological determinants of smart watch adoption (i.e. affective quality (AQ), relative advantage (RA), mobility (MB), availability (AV), subcultural appeal) and develops an extended technology acceptance model (TAM) that integrates the findings into the original TAM constructs. Design/methodology/approach - An online survey assessed the proposed psychological determinants of smart watch adoption. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were conducted on collected data ( n =363) using the AMOS 22 statistical software. The reliability and validity of the measurement assessing the proposed factor structure were examined via CFA, while the strength and direction of the hypothesized causal paths among the constructs were analyzed via SEM. Findings - The AQ and RA of smart watches were found to be associated with perceived usefulness, while the sense of MB and AV induced by smart watches led to a greater perceived ease of the technology's use. The results also indicated that the devices' subcultural appeal and cost were notable antecedents of user attitude (AT) and intention to use, respectively. Originality/value - Though smart watches are becoming increasingly popular, empirical studies on user perceptions of and ATs toward - them remain preliminary. This paper is one of the first scholarly attempts at a systematic prediction of smart watch usage, with implications for the adoption of future wearable technology.

Stress has been identified as one of the major causes of automobile crashes which then lead to high rates of fatalities and injuries each year. Stress can be measured via physiological measurements and in this study the focus will be based on the features that can be extracted by common wearable devices. Hence, the study will be mainly focusing on heart rate variability (HRV). This study is aimed at investigating the role of HRV-derived features as stress markers. This is achieved by developing a good predictive model that can accurately classify stress levels from ECG-derived HRV features, obtained from automobile drivers, by testing different machine learning methodologies such as K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Random Forest (RF) and Gradient Boosting (GB). Moreover, the models obtained with highest predictive power will be used as reference for the development of a machine learning model that would be used to classify stress from HRV features derived from heart rate measurements obtained from wearable devices. We demonstrate that HRV features constitute good markers for stress detection as the best machine learning model developed achieved a Recall of 80%. Furthermore, this study indicates that HRV metrics such as the Average of normal-to-normal (NN) intervals (AVNN), Standard deviation of the average NN intervals (SDNN) and the Root mean square differences of successive NN intervals (RMSSD) were important features for stress detection. The proposed method can be also used on all applications in which is important to monitor the stress levels in a non-invasive manner, e.g., in physical rehabilitation, anxiety relief or mental wellbeing.

Internet of things (IoT) cloud-based applications deliver advanced solutions for smart cities to decrease traffic accidents caused by driver fatigue while driving on the road. Environmental conditions or driver behavior can ultimately lead to serious roadside accidents. In recent years, the authors have developed many low-cost, computerized, driver fatigue detection systems (DFDs) to help drivers, by using multi-sensors, and mobile and cloud-based computing architecture. To promote safe driving, these are the most current emerging platforms that were introduced in the past. In this paper, we reviewed state-of-the-art approaches for predicting unsafe driving styles using three common IoT-based architectures. The novelty of this article is to show major differences among multi-sensors, smartphone-based, and cloud-based architectures in multimodal feature processing. We discussed all of the problems that machine learning techniques faced in recent years, particularly the deep learning (DL) model, to predict driver hypovigilance, especially in terms of these three IoT-based architectures. Moreover, we performed state-of-the-art comparisons by using driving simulators to incorporate multimodal features of the driver. We also mention online data sources in this article to test and train network architecture in the field of DFDs on public available multimodal datasets. These comparisons assist other authors to continue future research in this domain. To evaluate the performance, we mention the major problems in these three architectures to help researchers use the best IoT-based architecture for detecting DFDs in a real-time environment. Moreover, the important factors of Multi-Access Edge Computing (MEC) and 5th generation (5G) networks are analyzed in the context of deep learning architecture to improve the response time of DFD systems. Lastly, it is concluded that there is a research gap when it comes to implementing the DFD systems on MEC and 5G technologies by using multimodal features and DL architecture.

Telemedicine gained an increasing use throughout the last years. Lifestyle tools like the Apple watch seem to have an increasing spread even in remote areas and underdeveloped regions. The increasing availability of these tools offers the chance to use the health care functions of these devices to improve provision of professional medical care. First data on the use of the Apple Watch as a remote monitoring device in children have been reported, showing good acceptability and usability of the Apple Watch for symptom monitoring in children. This study aimed to evaluate the accuracy of the Apple Watch iECG in comparison to a standard 12-lead ECG in pre-term babies.INTRODUCTIONTelemedicine gained an increasing use throughout the last years. Lifestyle tools like the Apple watch seem to have an increasing spread even in remote areas and underdeveloped regions. The increasing availability of these tools offers the chance to use the health care functions of these devices to improve provision of professional medical care. First data on the use of the Apple Watch as a remote monitoring device in children have been reported, showing good acceptability and usability of the Apple Watch for symptom monitoring in children. This study aimed to evaluate the accuracy of the Apple Watch iECG in comparison to a standard 12-lead ECG in pre-term babies.In this prospective, single-arm study, consecutive preterm neonates hospitalised in Leipzig University Hospital neonatal ICU were eligible. A 12-lead ECG and an iECG using Apple Watch 4 were performed. iECG and 12-lead ECG measurements were performed by a paediatric cardiologist. Cardiac rhythm was classified and amplitudes and timing intervals were analysed for comparability.METHODSIn this prospective, single-arm study, consecutive preterm neonates hospitalised in Leipzig University Hospital neonatal ICU were eligible. A 12-lead ECG and an iECG using Apple Watch 4 were performed. iECG and 12-lead ECG measurements were performed by a paediatric cardiologist. Cardiac rhythm was classified and amplitudes and timing intervals were analysed for comparability.Fifty preterm neonates, gestational week (23-36 weeks), and body weight (0.65-3.09 kg) were enrolled. Overall good quality and excellent correlation of the Apple Watch generated iECG in comparison to the standard 12-lead ECG could be demonstrated (p < 0.001). When interpreted by a paediatric cardiologist, a correct rhythm classification could be done in 100% of cases.RESULTSFifty preterm neonates, gestational week (23-36 weeks), and body weight (0.65-3.09 kg) were enrolled. Overall good quality and excellent correlation of the Apple Watch generated iECG in comparison to the standard 12-lead ECG could be demonstrated (p < 0.001). When interpreted by a paediatric cardiologist, a correct rhythm classification could be done in 100% of cases.The Apple Watch iECG seems to be a valuable tool to record an ECG comparable to lead I of the standard 12-lead ECG even in pre-term neonates. With a widespread availability and excellent connectivity, the Apple Watch iECG function may provide practitioners with a tool to send an iECG for interpretation to a paediatric cardiac specialist.CONCLUSIONThe Apple Watch iECG seems to be a valuable tool to record an ECG comparable to lead I of the standard 12-lead ECG even in pre-term neonates. With a widespread availability and excellent connectivity, the Apple Watch iECG function may provide practitioners with a tool to send an iECG for interpretation to a paediatric cardiac specialist.

This study sought to assess the performance of the Fitbit Charge HR, a consumer-level multi-sensor activity tracker, to measure physical activity and sleep in children. 59 healthy boys and girls aged 9-11 years old wore a Fitbit Charge HR, and accuracy of physical activity measures were evaluated relative to research-grade measures taken during a combination of 14 standardized laboratory- and field-based assessments of sitting, stationary cycling, treadmill walking or jogging, stair walking, outdoor walking, and agility drills. Accuracy of sleep measures were evaluated relative to polysomnography (PSG) in 26 boys and girls during an at-home unattended PSG overnight recording. The primary analyses included assessment of the agreement (biases) between measures using the Bland-Altman method, and epoch-by-epoch (EBE) analyses on a minute-by-minute basis. Fitbit Charge HR underestimated steps ( 11.8 steps per minute), heart rate ( 3.58 bpm), and metabolic equivalents ( 0.55 METs per minute) and overestimated energy expenditure ( 0.34 kcal per minute) relative to research-grade measures (p< 0.05). The device showed an overall accuracy of 84.8% for classifying moderate and vigorous physical activity (MVPA) and sedentary and light physical activity (SLPA) (sensitivity MVPA: 85.4%; specificity SLPA: 83.1%). Mean estimates of bias for measuring total sleep time, wake after sleep onset, and heart rate during sleep were 14 min, 9 min, and 1.06 bpm, respectively, with 95.8% sensitivity in classifying sleep and 56.3% specificity in classifying wake epochs. Fitbit Charge HR had adequate sensitivity in classifying moderate and vigorous intensity physical activity and sleep, but had limitations in detecting wake, and was more accurate in detecting heart rate during sleep than during exercise, in healthy children. Further research is needed to understand potential challenges and limitations of these consumer devices.

Background: Detecting of human movements is an important task in various areas such as healthcare, fitness and eldercare. It is now possible to achieve this aim using mobile applications. These applications provide users, doctors and related persons a better understanding about daily physical activities. It can also lead to various useful habits by following the activities of the users in their daily life. In addition, dangerous actions such as the fall of elderly people or young children are identified and necessary precautions are taken as soon as possible. Classification of human motions with motion sensor data is among the current topics of study. Smart watches have these sensors built-in. Thus, it is possible to follow the activities of a user carrying only a smart watch. Methods: The purpose of this work is to detect human movements using smart watch sensor data and machine learning methods. The data are obtained from the accelerometer, gyroscope, step counter and heart rate sensors of the smart watch. The obtained data have been divided into 2 s windows and a data set containing 500 patterns for each class has been created for each class. Results and Discussion: After the features were determined, the data set to which the principal component analysis has been applied was classified by random forest, support vector machine, C4.5 and k-nearest neighbor methods, and their performances were compared. The most successful result was obtained from the random forest method.