Wearable systems are commonly used for fitness purpose as these devices provide activity measurements to motivate daily exercise. With aims to promote improved health, healthcare companies are incentivizing their customers with the amount of exercise that is performed and using readings from wearable devices as a way of proving that the individual met the requirements. However, these devices have a risk of user spoofing attacks as an unauthorized individual can utilize the system. To prevent misuse of the product to gain reward and ultimately promote daily exercise for various types of exercise reward systems, we propose a biometric gait identification approach using a smart earring that we design and develop. In this paper, we preliminary train and test the gait identification system by utilizing a transfer learning, which shows a 100% classification performance for eight participants. We expect the proposed gait identification technique will serve as essential building blocks for reliable exercise reward systems.

With the advancement of technology in various domains, many efforts have been made to design advanced classification engines that aid the protection of civilians and their properties in different settings. In this work, we focus on a set of the population which is probably the most vulnerable: children. Specifically, we present ChildSafe , a classification system that exploits ratios of skeletal features extracted from children and adults using a 3D depth camera to classify visual characteristics between the two age groups. Specifically, we combine the ratio information into one bag-of-words feature for each sample, where each word is a histogram of the ratios. ChildSafe analyzes the words that are normalized within and between the two age groups and implements a fuzzy bin-based classification method that represents bin-boundaries using fuzzy sets. We train and evaluate ChildSafe using a large dataset of visual samples collected from 150 elementary school children and 150 adults, ranging in age from 7 to 50. Our results suggest that ChildSafe successfully detects children with a proper classification rate of up to 94%, a false-negative rate as low as 1.82%, and a low false-positive rate of 5.14%. We envision this work as a first step, an effective subsystem for designing child safety applications.

It is crucial that cardiovascular disease patients pay close attention to their heartrate since the elevation of his or her heartrate may lead to critical conditions. Many efforts are made to help monitor heartrate by using wearables. However, these devices project inaccurate readings due to motion artifacts. We evaluate the accuracy of different smart watches in measuring heartrate, design our own filtering algorithm, and validate it with real hospital, clinical-scale devices to improve the quality of disease detection on wearable devices. It is important to note that we plan to make this algorithm as a module or component to use in various applications.

One of fields that takes advantage of IoT is home automation. Home automation uses different types of network protocols such as Wi-Fi, Bluetooth and ZigBee. However, existing home equipment often requires network communication enabled power plugs or devices that has a unique communication protocol specified by the company. Although these equipment have standard communication capabilities, each device is limited to communicate within a same network protocol enabled devices. In order to solve this issue between various network protocols in smart homes, we present Smart Home Automation Framework (SHAF), which is a framework that use Raspberry Pi with ZigBee as smart home central server and Arduino with ZigBee as sensor nodes to control smart home environment.

With an increased awareness of living a health-conscious lifestyle, many efforts are made to reduce stress levels. However, activities such as exercising require an action to go to a certain location and effort to set aside an allocated time. To effectively contribute towards lowering stress levels, we propose convenient, entertaining, and time-effective ways of relieving stress with objects that are used and seen inside home and workplaces. Relieving stress within daily routines can greatly contribute towards making home and workplace more enjoyable, saving time, and improving physical, emotional and psychological health. Specifically, we present: (1) a magic mirror, (2) an interactive media wall, (3) an interactive plant, and (4) an interactive living room wall. We deploy these in real-world environments to demonstrate how interactive objects can play an important role in home and workplaces.

With the advancement of technology in various domains, many efforts have been made to design state-of-the-art classification engines using depth cameras. Being inspired by its potential of providing information at the skeleton level using a non-invasive infrared camera, many studies have been done to aid vulnerable people such as children, elderly, and people that are physically or mentally ill. However, most of these studies focus on the algorithms and processing of a single camera, and do not consider issues that are found in practical deployments. We present KinFrame, a framework that considers challenges and requirements of designing practical systems for vulnerable people and allows application developers to easily and efficiently setup large scale, multiple depth camera deployment.

Diaries are used to record aspects of lives --- activities, events, experiences, feelings, thoughts, and physiological measures. Smart diaries can reduce the user's burden by automatically registering some of these aspects. Existing systems have two weaknesses: (a) they are not extensible, and (b) their design is not theory-driven. We introduce LifeMaps, a cloud-based automated, extensible, theory-driven smart diary. Because it is inspired by theory, "The Deep Structures of Lives," it organizes data to conform to the way we experience our lives and may lead a deeper understanding of lives.

Smart watches are increasingly being used in various applications to monitor heart rate for exercise and health care purposes. It is crucial that the readings from these devices are accurate so that users can take proper actions according to the intensity of the heart rate. Taking actions from inaccurate readings can negatively impact the health of the user. In this work, we run a preliminary study that verifies the accuracy of wearable platforms by comparing the measurements with a clinically-grade device.

AsthmaGuide is a smartphone and cloud based asthma system in which a smart phone is used as a hub for collecting a comprehensive collection of information. The data, including data over time, is then displayed in a cloud web application for both patients and healthcare providers to view. AsthmaGuide also provides an advice and alarm infrastructure based on the collected data and parameters set by healthcare providers. With these components, AsthmaGuide provides a comprehensive ecosystem that allows patients to be involved in their own health and also allows doctors to provide more effective day to day care. Using real asthma patient wheezing sounds we develop a new combination of classifiers that is 96% accurate at automatically detecting wheezing. This abstract provides an overview of the design and implementation of AsthmaGuide and provides empirical evidence that AsthmaGuide is 3% - 11% more accurate in detecting wheezing sounds than standard techniques.

In this work, we present ChildSafe, a classification system which exploits human skeletal features collected using a 3D depth camera to classify visual characteristics between children and adults. ChildSafe analyzes the histograms of training samples and implements a bin-boundary-based classifier. We train and evaluate ChildSafe using a large dataset of visual samples collected from 150 elementary school children and 43 adults, ranging in the ages of 7 and 50. Our results suggest that ChildSafe successfully detects children with a proper classification rate of up to 97%, a false negative rate of as low as 1.82%, and a low false positive rate of 1.46%. We envision this work as an effective sub-system for designing various child protection applications.

Kintense is a robust, accurate, real-time, and evolving system for detecting aggressive actions such as hitting, kicking, pushing, and throwing from streaming 3D skeleton joint coordinates obtained from Kinect sensors. Kintense uses a combination of: (1) an array of supervised learners to recognize a predefined set of aggressive actions, (2) an unsupervised learner to discover new aggressive actions or refine existing actions, and (3) human feedback to reduce false alarms and to label potential aggressive actions. This paper describes the design and implementation of Kintense and provides empirical evidence that the system is 11% - 16% more accurate and 10% - 54% more robust to changes in distance, body orientation, speed, and person when compared to standard techniques such as dynamic time warping (DTW) and posture based gesture recognizers. We deploy Kintense in two multi-person households and demonstrate how it evolves to discover and learn unseen actions, achieves up to 90% accuracy, runs in real-time, and reduces false alarms with up to 13 times fewer user interactions than a typical system.

Falls are a significant problem for the elderly living independently in the home. Many falls occur due to household objects left in open spaces. We present KinSpace, a system that uses real-time depth data and human-in-the-loop feedback to adjust its understanding of the open space of an environment. We present results showing the effectiveness of our underlying technical solutions in identifying open spaces and obstacles. The results for both lab testing and a small deployment in an actual home show over 80% accuracy for open space detection and 70% accuracy in obstacle detection even in the presence of many real world issues.

Kintense is a robust, accurate, real-time, and evolving system for detecting aggressive actions such as hitting, kicking, pushing, and throwing from streaming 3D skeleton joint coordinates obtained from Kinect sensors. Kintense uses a combination of: (1) an array of supervised learners to recognize a predefined set of aggressive actions, (2) an unsupervised learner to discover new aggressive actions or refine existing actions, and (3) human feedback to reduce false alarms and to label potential aggressive actions. This abstract provides an overview of the design and implementation of Kintense and provides empirical evidence that Kintense is 11% -- 16% more accurate when compared to standard techniques such as dynamic time warping (DTW) and posture based gesture recognizers.