Exploring Significant Motion Sensor for Energy-efficient Continuous Motion and Location Sampling in Mobile Sensing Application

The significant motion sensor is a new sensor that promises motion detection at low power consumption. Despite that promise, no known research has explored the usage of this sensor, especially in mobile sensing research. In this study, we explore the utilization of this significant motion sensor for continuous motion and location sampling in a mobile sensing application. A location sensor is known for its expensive power consumption in retrieving the location data, and continuously sampling from it will quickly deplete a smartphone battery. We experiment with two sampling strategies that utilize this significant motion sensor to achieve low power consumption during continuous sampling. One strategy involves utilizing the sensor naively, while the other involves combining with the duty cycle. Both strategies achieve low energy consumption, but the one that combines with the duty cycle achieves lower energy consumption. By utilizing this sensor, mobile sensing research especially that samples data from location or motion sensors, will be able to achieve lower energy consumption

Adopting Digital Solutions for Large Scale Surveillance of Crop Pests and Diseases in Developing Countries—A Review

Crop pests and diseases are ranked as some of the world’s leading threats to agricultural productivity. The need to improve adoption of digital solutions prompted a review on the applicability of emerging digital solutions in large-scale surveillance of crop pest and diseases. This study presents findings on key requirements for achieving digitized large-scale pest surveillance, fitness for purpose of common autonomous biosecurity surveillance technologies, and prospects of smartphones as an alternative surveillance solution. Firstly, the research identified appropriateness of the solution, availability of supporting infrastructure and level of stakeholder involvement in solution formulation as some of the key determinants of digital solution adoption. Although most common autonomous biosecurity surveillance technologies are promising, their adoption in developing nations are limited by operational costs, legal requirements, skillsets, and operational environments among others. Thirdly, recent advancements in smartphones and wide spread ownership among farmers provide a unique opportunity for advancing Mobile Crowd-Sensing solutions in achieving large-scale pest surveillance. Lastly, we recommend designing an incentive mechanism to motivate farmers’ participation in a surveillance solution

Understanding the interaction between human activities and physical health under extreme heat environment in Phoenix, Arizona

Long-term community resilience, which privileges a long view look at chronic issues influencing communities, has begun to draw more attention from city planners, researchers and policymakers. In Phoenix, resilience to heat is both a necessity and a way of life. In this paper, we attempt to understand how residents living in Phoenix experience and behave in an extreme heat environment. To achieve this goal, we introduced a smartphone application (ActivityLog) to study spatio-temporal dynamics of human interaction with urban environments. Compared with traditional paper activity log results we have in this study, the smartphone-based activity log has higher data quality in terms of total number of logs, response rates, accuracy, and connection with GPS and temperature sensors. The research results show that low-income residents in Phoenix mostly stay home during the summer but experience a relatively high indoor temperature due to the lack/low efficiency of air-conditioning (AC) equipment or lack of funds to run AC frequently. Middle-class residents have a better living experience in Phoenix with better mobility with automobiles and good quality of AC. The research results help us better understand user behaviors for daily log activities and how human activities interact with the urban thermal environment, informing further planning policy development. The ActivityLog smartphone application is also presented as an open-source prototype to design a similar urban climate citizen science program in the future

Logging Stress and Anxiety Using a Gamified Mobile-based EMA Application, and Emotion Recognition Using a Personalized Machine Learning Approach

According to American Psychological Association (APA) more than 9 in 10 (94 percent) adults believe that stress can contribute to the development of major health problems, such as heart disease, depression, and obesity. Due to the subjective nature of stress, and anxiety, it has been demanding to measure these psychological issues accurately by only relying on objective means. In recent years, researchers have increasingly utilized computer vision techniques and machine learning algorithms to develop scalable and accessible solutions for remote mental health monitoring via web and mobile applications. To further enhance accuracy in the field of digital health and precision diagnostics, there is a need for personalized machine-learning approaches that focus on recognizing mental states based on individual characteristics, rather than relying solely on general-purpose solutions. This thesis focuses on conducting experiments aimed at recognizing and assessing levels of stress and anxiety in participants. In the initial phase of the study, a mobile application with broad applicability (compatible with both Android and iPhone platforms) is introduced (we called it STAND). This application serves the purpose of Ecological Momentary Assessment (EMA). Participants receive daily notifications through this smartphone-based app, which redirects them to a screen consisting of three components. These components include a question that prompts participants to indicate their current levels of stress and anxiety, a rating scale ranging from 1 to 10 for quantifying their response, and the ability to capture a selfie. The responses to the stress and anxiety questions, along with the corresponding selfie photographs, are then analyzed on an individual basis. This analysis focuses on exploring the relationships between self-reported stress and anxiety levels and potential facial expressions indicative of stress and anxiety, eye features such as pupil size variation and eye closure, and specific action units (AUs) observed in the frames over time. In addition to its primary functions, the mobile app also gathers sensor data, including accelerometer and gyroscope readings, on a daily basis. This data holds potential for further analysis related to stress and anxiety. Furthermore, apart from capturing selfie photographs, participants have the option to upload video recordings of themselves while engaging in two neuropsychological games. These recorded videos are then subjected to analysis in order to extract pertinent features that can be utilized for binary classification of stress and anxiety (i.e., stress and anxiety recognition). The participants that will be selected for this phase are students aged between 18 and 38, who have received recent clinical diagnoses indicating specific stress and anxiety levels. In order to enhance user engagement in the intervention, gamified elements - an emerging trend to influence user behavior and lifestyle - has been utilized. Incorporating gamified elements into non-game contexts (e.g., health-related) has gained overwhelming popularity during the last few years which has made the interventions more delightful, engaging, and motivating. In the subsequent phase of this research, we conducted an AI experiment employing a personalized machine learning approach to perform emotion recognition on an established dataset called Emognition. This experiment served as a simulation of the future analysis that will be conducted as part of a more comprehensive study focusing on stress and anxiety recognition. The outcomes of the emotion recognition experiment in this study highlight the effectiveness of personalized machine learning techniques and bear significance for the development of future diagnostic endeavors. For training purposes, we selected three models, namely KNN, Random Forest, and MLP. The preliminary performance accuracy results for the experiment were 93%, 95%, and 87% respectively for these models