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

Interaction With Tilting Gestures In Ubiquitous Environments

In this paper, we introduce a tilting interface that controls direction based applications in ubiquitous environments. A tilt interface is useful for situations that require remote and quick interactions or that are executed in public spaces. We explored the proposed tilting interface with different application types and classified the tilting interaction techniques. Augmenting objects with sensors can potentially address the problem of the lack of intuitive and natural input devices in ubiquitous environments. We have conducted an experiment to test the usability of the proposed tilting interface to compare it with conventional input devices and hand gestures. The experiment results showed greater improvement of the tilt gestures in comparison with hand gestures in terms of speed, accuracy, and user satisfaction.Comment: 13 pages, 10 figure

Enabling mobile microinteractions

While much attention has been paid to the usability of desktop computers, mobile com- puters are quickly becoming the dominant platform. Because mobile computers may be used in nearly any situation--including while the user is actually in motion, or performing other tasks--interfaces designed for stationary use may be inappropriate, and alternative interfaces should be considered. In this dissertation I consider the idea of microinteractions--interactions with a device that take less than four seconds to initiate and complete. Microinteractions are desirable because they may minimize interruption; that is, they allow for a tiny burst of interaction with a device so that the user can quickly return to the task at hand. My research concentrates on methods for applying microinteractions through wrist- based interaction. I consider two modalities for this interaction: touchscreens and motion- based gestures. In the case of touchscreens, I consider the interface implications of making touchscreen watches usable with the finger, instead of the usual stylus, and investigate users' performance with a round touchscreen. For gesture-based interaction, I present a tool, MAGIC, for designing gesture-based interactive system, and detail the evaluation of the tool.Ph.D.Committee Chair: Starner, Thad; Committee Member: Abowd, Gregory; Committee Member: Isbell, Charles; Committee Member: Landay, james; Committee Member: McIntyre, Blai

Seizure Detection, Seizure Prediction, and Closed-Loop Warning Systems in Epilepsy

Nearly one-third of patients with epilepsy continue to have seizures despite optimal medication management. Systems employed to detect seizures may have the potential to improve outcomes in these patients by allowing more tailored therapies and might, additionally, have a role in accident and SUDEP prevention. Automated seizure detection and prediction require algorithms which employ feature computation and subsequent classification. Over the last few decades, methods have been developed to detect seizures utilizing scalp and intracranial EEG, electrocardiography, accelerometry and motion sensors, electrodermal activity, and audio/video captures. To date, it is unclear which combination of detection technologies yields the best results, and approaches may ultimately need to be individualized. This review presents an overview of seizure detection and related prediction methods and discusses their potential uses in closed-loop warning systems in epilepsy

Opportunistic human activity and context recognition

Although the Internet of Things allows seamless access to billions of sensors readily deployed throughout the world, current context- and activity-recognition approaches restrict ambient intelligence to domains where dedicated sensors are deployed. The big data delivered by the Internet of Things calls for a new opportunistic recognition paradigm. Instead of setting-up information sources for a specific recognition goal, the methods themselves adapt to the data available at any time. We present enabling methods that allow for opportunistic recognition in dynamic sensor configurations. This could be the missing link to fulfill the promise of ambient intelligence anywhere

Smoking Gesture Detection in Natural Settings

Since 1964, more than 20 million Americans have died as a result of smoking. Being able to reflect on smoking habits allows smokers to quit more easily. By using a smartwatch features can be extracted as a tool to detect smoking gestures. Smoking gesture detection used machine learning to detect patterns from various features including max, median, mean, and variance speed, and net, median, and max roll velocity. The machine learning algorithm then classified which gesture was being performed based on training data of 30 people performing 4 distinct gestures 30 times each. The WEKA data mining library tested multiple classification algorithms and the most accurate was Random Forest. Successful detection of these smoking gestures can help smokers quit by providing real time interjections

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

MsWH: A multi-sensory hardware platform for capturing and analyzing physiological emotional signals

This paper presents a new physiological signal acquisition multi-sensory platform for emotion detection: Multi-sensor Wearable Headband (MsWH). The system is capable of recording and analyzing five different physiological signals: skin temperature, blood oxygen saturation, heart rate (and its variation), movement/position of the user (more specifically of his/her head) and electrodermal activity/bioimpedance. The measurement system is complemented by a porthole camera positioned in such a way that the viewing area remains constant. Thus, the user''s face will remain centered regardless of its position and movement, increasing the accuracy of facial expression recognition algorithms. This work specifies the technical characteristics of the developed device, paying special attention to both the hardware used (sensors, conditioning, microprocessors, connections) and the software, which is optimized for accurate and massive data acquisition. Although the information can be partially processed inside the device itself, the system is capable of sending information via Wi-Fi, with a very high data transfer rate, in case external processing is required. The most important features of the developed platform have been compared with those of a proven wearable device, namely the Empatica E4 wristband, in those measurements in which this is possible