Wearable sensor-based systems and devices have been expanded in different application domains, especially in the healthcare arena. Automatic age and gender estimation has several important applications. Gait has been demonstrated as a profound motion cue for various applications. A gait-based age and gender estimation challenge was launched in the 12th IAPR International Conference on Biometrics (ICB), 2019. In this competition, 18 teams initially registered from 14 countries. The goal of this challenge was to find some smart approaches to deal with age and gender estimation from sensor-based gait data. For this purpose, we employed a large wearable sensor-based gait dataset, which has 745 subjects (357 females and 388 males), from 2 to 78 years old in the training dataset; and 58 subjects (19 females and 39 males) in the test dataset. It has several walking patterns. The gait data sequences were collected from three IMUZ sensors, which were placed on waist-belt or at the top of a backpack. There were 67 solutions from ten teams—for age and gender estimation. This paper extensively analyzes the methods and achieved-results from various approaches. Based on analysis, we found that deep learning-based solutions lead the competitions compared with conventional handcrafted methods. We found that the best result achieved 24.23% prediction error for gender estimation, and 5.39 mean absolute error for age estimation by employing angle embedded gait dynamic image and temporal convolution network.

Sensor-based human activity recognition has various applications in the arena of healthcare, elderly smart-home, sports, etc. There are numerous works in this field—to recognize various human activities from sensor data. However, those works are based on data patterns that are clean data and have almost no missing data, which is a genuine concern for real-life healthcare centers. Therefore, to address this problem, we explored the sensor-based activity recognition when some partial data were lost in a random pattern. In this paper, we propose a novel method to improve activity recognition while having missing data without any data recovery. For the missing data pattern, we considered data to be missing in a random pattern, which is a realistic missing pattern for sensor data collection. Initially, we created different percentages of random missing data only in the test data, while the training was performed on good quality data. In our proposed approach, we explicitly induce different percentages of missing data randomly in the raw sensor data to train the model with missing data. Learning with missing data reinforces the model to regulate missing data during the classification of various activities that have missing data in the test module. This approach demonstrates the plausibility of the machine learning model, as it can learn and predict from an identical domain. We exploited several time-series statistical features to extricate better features in order to comprehend various human activities. We explored both support vector machine and random forest as machine learning models for activity classification. We developed a synthetic dataset to empirically evaluate the performance and show that the method can effectively improve the recognition accuracy from 80.8% to 97.5%. Afterward, we tested our approach with activities from two challenging benchmark datasets: the human activity sensing consortium (HASC) dataset and single chest-mounted accelerometer dataset. We examined the method for different missing percentages, varied window sizes, and diverse window sliding widths. Our explorations demonstrated improved recognition performances even in the presence of missing data. The achieved results provide persuasive findings on sensor-based activity recognition in the presence of missing data.

The rise in mental health disorders and the demand for appropriate medical treatment have motivated researchers to look into machine learning (ML) applications in mental health. The use of ML approaches may open up new prospects for understanding human behavior patterns, detecting risk factors and symptoms associated with mental disorders, making predictions about the course of diseases, and personalizing and improving treatment. Despite the potential benefits of applying ML to the field of mental health, this is still a relatively new area of research, and creating practical, ML-enabled applications is complicated with a number of intricate, interrelated problems. In this paper, we reviewed machine learning methods for predicting mental health issues. We categorize the collected research articles based on the mental health problems such as schizophrenia, bipolar disorder, anxiety, depression, and stress. Our findings show that translating physiological and behavioral data into mental health phenotypes and states is not simple and must be well-established. Most existing monitoring systems do not evaluate actual patient data, focusing instead on the general people and healthy participants for health and fitness applications. Also, we find that when introducing a predictive model, user-independent (general) models need to consider more diversities like age and ethnicity. Thus, the current review aims to present and evaluate all available smartphone-based, wearable, and environmental sensors for detecting physiological and behavioral pattern, existing approaches, challenges and difficulties experienced by the researchers for understanding the most prevalent mental health disorders. <!----> <!----> <!----> <!---->

Computational models that formalize complex human behaviors enable study and understanding of such behaviors. However, collecting behavior data required to estimate the parameters of such models is often tedious and resource intensive. Thus, estimating dataset size as part of data collection planning (also known as Sample Size Determination) is important to reduce the time and effort of behavior data collection while maintaining an accurate estimate of model parameters. In this article, we present a sample size determination method based on Uncertainty Quantification (UQ) for a specific Inverse Reinforcement Learning (IRL) model of human behavior, in two cases: (1)pre-hocexperiment design—conducted in the planning stage before any data is collected, to guide the estimation of how many samples to collect; and (2)post-hocdataset analysis—performed after data is collected, to decide if the existing dataset has sufficient samples and whether more data is needed. We validate our approach in experiments with a realistic model of behaviors of people with Multiple Sclerosis (MS) and illustrate how to pick a reasonable sample size target. Our work enables model designers to perform a deeper, principled investigation of the effects of dataset size on IRL model parameters.

Recognizing user emotions while watching videos is critical for video content modification and personalisation. Many research attempted to objectively assess the viewer’s perception by utilizing different devices and vital data. However, such equipment might add to the strain and psychological stress of watching a movie, affecting the overall experience. Smartwatches now have pulse rate measuring technologies, and wearing one has no negative impact on daily life. Therefore, in this research, we proposed using a smartwatch to objectively assess emotions while watching a video. For that purpose, in this study, we clarified the relationship between heart rate variability (HRV) measured with a smartwatch and emotions when watching videos. We measured HRV from 10 people watching alone a 10-minute horror video and extracted 11 features from both frequency and time domain to predict two emotions ‘Fear’ and ‘Fearlessness Situation’ while watching a horror movie. We evaluated the effect of feature extraction, window characteristics and machine learning model to evaluate the prediction accuracy As a result, the Support Vector Machine (SVM) model, fed with the features extracted from the 60 seconds following the horror event, obtained the best average F1 value of 90%.