Sensor-AssistedWeighted Average Ensemble Model for Detecting Major Depressive Disorder

The present methods of diagnosing depression are entirely dependent on self-report ratings or clinical interviews. Those traditional methods are subjective, where the individual may or may not be answering genuinely to questions. In this paper, the data has been collected using self-report ratings and also using electronic smartwatches. This study aims to develop a weighted average ensemble machine learning model to predict major depressive disorder (MDD) with superior accuracy. The data has been pre-processed and the essential features have been selected using a correlation-based feature selection method. With the selected features, machine learning approaches such as Logistic Regression, Random Forest, and the proposedWeighted Average Ensemble Model are applied. Further, for assessing the performance of the proposed model, the Area under the Receiver Optimization Characteristic Curves has been used. The results demonstrate that the proposed Weighted Average Ensemble model performs with better accuracy than the Logistic Regression and the Random Forest approaches

Validity of telemetric-derived measures of heart rate variability: a systematic review

Heart rate variability (HRV) is a widely accepted indirect measure of autonomic function with widespread application across many settings. Although traditionally measured from the 'gold standard' criterion electrocardiography (ECG), the development of wireless telemetric heart rate monitors (HRMs) extends the scope of the HRV measurement. However, the validity of telemetric-derived data against the criterion ECG data is unclear. Thus, the purpose of this study was twofold: (a) to systematically review the validity of telemetric HRM devices to detect inter-beat intervals and aberrant beats; and (b) to determine the accuracy of HRV parameters computed from HRM-derived inter-beat interval time series data against criterion ECG-derived data in healthy adults aged 19 to 62 yrs. A systematic review of research evidence was conducted. Four electronic databases were accessed to obtain relevant articles (PubMed, EMBASE, MEDLINE and SPORTDiscus. Articles published in English between 1996 and 2016 were eligible for inclusion. Outcome measures included temporal and power spectral indices (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996). The review confirmed that modern HRMs (Polar® V800™ and Polar® RS800CX™) accurately detected inter-beat interval time-series data. The HRV parameters computed from the HRM-derived time series data were interchangeable with the ECG-derived data. The accuracy of the automatic in-built manufacturer error detection and the HRV algorithms were not established. Notwithstanding acknowledged limitations (a single reviewer, language bias, and the restricted selection of HRV parameters), we conclude that the modern Polar® HRMs offer a valid useful alternative to the ECG for the acquisition of inter-beat interval time series data, and the HRV parameters computed from Polar® HRM-derived inter-beat interval time series data accurately reflect ECG-derived HRV metrics, when inter-beat interval data are processed and analyzed using identical protocols, validated algorithms and software, particularly under controlled and stable conditions

Increasing compliance with wearing a medical device in children with autism

Health professionals often recommend the use of medical devices to assess the health, monitor the well-being, or improve the quality of life of their patients. Children with autism may present challenges in these situations as their sensory peculiarities may increase refusals to wear such devices. To address this issue, we systematically replicated prior research by examining the effects of differential reinforcement of other behavior (DRO) to increase compliance with wearing a heart rate monitor in 2 children with autism. The intervention increased compliance to 100% for both participants when an edible reinforcer was delivered every 90 s. The results indicate that DRO does not require the implementation of extinction to increase compliance with wearing a medical device. More research is needed to examine whether the reinforcement schedule can be further thinned

Applying Machine Learning Techniques to Categorize and Reduce Stress in Human Beings

The number of individuals in the modernworld experience elevated stress level, which is non-specific response on the body and plays a significant toll on health, productivity at work, relationships and also effect overall well-being. Many individuals are not aware of the stress triggers and potential health problems caused by prolonged stress. In order to effectively combat stress and its ill effects on health, stress triggers and responses to stress must be recognized and managed in real time. In this paper, applications of machine learning techniques are suggested to categorize and reduce stress is explored. The idea of monitoring stress and reducingstress usesmethods like personalized music, wallpaper themes, favorite games or favorite food ordering and so on. Activities which reduce stress and their degree of reduction are monitored in real time and based on customized stress reduction portfolio is designed using machine learning algorithms

Wearable Computing for Health and Fitness: Exploring the Relationship between Data and Human Behaviour

Health and fitness wearable technology has recently advanced, making it easier for an individual to monitor their behaviours. Previously self generated data interacts with the user to motivate positive behaviour change, but issues arise when relating this to long term mention of wearable devices. Previous studies within this area are discussed. We also consider a new approach where data is used to support instead of motivate, through monitoring and logging to encourage reflection. Based on issues highlighted, we then make recommendations on the direction in which future work could be most beneficial

Standardized exercise tests in horses : current situation and future perspectives

The purpose of this literature review is to clarify how exercise capacity can be measured in horses and which standardized exercise tests (SETs) exist. In this review, the measurement of the exercise capacity of horses is discussed and the standardized exercise tests (SET) are described. Two main types of SETs are used. Laboratory or treadmill tests are easy to standardize and provide more options to use all kinds of measuring devices, since the horse stays on the treadmill. On the other hand, field tests are conducted under the natural conditions associated with the specific sports discipline, and are easier to implement in the training schedule. However, field tests encompass interfering variables, such as weather conditions, ground surface conditions and the rider or jockey. Several variables are measured in order to calculate the fitness level which may be expressed by different parameters, such as V200 (speed at a heart rate of 200 beats per minute), V1a4 (speed at a blood lactic acid level of 4 mmol/L) and VO2max (maximum oxygen uptake)