Tianxing Chu

Inferring Human Activity in Mobile Devices by Computing Multiple Contexts

This paper introduces a framework for inferring human activities in mobile devices by computing spatial contexts, temporal contexts, spatiotemporal contexts, and user contexts. A spatial context is a significant location that is defined as a geofence, which can be a node associated with a circle, or a polygon; a temporal context contains time-related information that can be e.g., a local time tag, a time difference between geographical locations, or a timespan; a spatiotemporal context is defined as a dwelling length at a particular spatial context; and a user context includes user-related information that can be the user’s mobility contexts, environmental contexts, psychological contexts or social contexts. Using the measurements of the built-in sensors and radio signals in mobile devices, we can snapshot a contextual tuple for every second including aforementioned contexts. Giving a contextual tuple, the framework evaluates the posteriori probability of each candidate activity in real-time using a Naïve Bayes classifier. A large dataset containing 710,436 contextual tuples has been recorded for one week from an experiment carried out at Texas A&M University Corpus Christi with three participants. The test results demonstrate that the multi-context solution significantly outperforms the spatial-context-only solution. A classification accuracy of 61.7% is achieved for the spatial-context-only solution, while 88.8% is achieved for the multi-context solution.

Reciprocal Estimation of Pedestrian Location and Motion State toward a Smartphone Geo-Context Computing Solution

The rapid advance in mobile communications has made information and services ubiquitously accessible. Location and context information have become essential for the effectiveness of services in the era of mobility. This paper proposes the concept of geo-context that is defined as an integral synthesis of geographical location, human motion state and mobility context. A geo-context computing solution consists of a positioning engine, a motion state recognition engine, and a context inference component. In the geo-context concept, the human motion states and mobility context are associated with the geographical location where they occur. A hybrid geo-context computing solution is implemented that runs on a smartphone, and it utilizes measurements of multiple sensors and signals of opportunity that are available within a smartphone. Pedestrian location and motion states are estimated jointly under the framework of hidden Markov models, and they are used in a reciprocal manner to improve their estimation performance of one another. It is demonstrated that pedestrian location estimation has better accuracy when its motion state is known, and in turn, the performance of motion state recognition can be improved with increasing reliability when the location is given. The geo-context inference is implemented simply with the expert system principle, and more sophisticated approaches will be developed.

A context detection approach using GPS module and emerging sensors in smartphone platform

Nowadays smartphones are equipped with various sensors and powerful processing modules, and are accessible to flexible communication networks, thus enabling complex applications such as context awareness, activity recognition, health care monitoring and so forth. These applications typically require contextual information to optimize the effectiveness, e.g. indoor/outdoor identification. This paper develops an indoor/outdoor detection method based on a generic smartphone platform, utilizing the information extracted from the internal clock, GPS module and light intensity sensor. The vote principle is used in the detection. The approach has been tested in multiple locations in order to evaluate performance. This includes residences, office space, roads, restaurants, markets and so forth. Two kinds of detection results consisting of static and walking scenarios are shown in the paper. This method can output detection results with good accuracy in both day and night and all weather conditions. The approach can operate on different smartphone profiles from low-end to high-end. An optimized method also presents for some advanced smartphones with GPS satellite signal noise ratio output, which has been shown more effective in real-time response and detection accuracy.

An Indoor and Outdoor Seamless Positioning System Based on Android Platform

The proliferation of powerful mobile devices, along with the increasing demand of location information, has driven the researches to develop positioning applications with higher accuracy. In this paper, we build an Android application which achieved the aim of seamless positioning between the inside and the outside. The system contains four parts: outdoor positioning, which uses GPS and Baidu Map, indoor positioning, which adopts Wi-Fi fingerprint positioning, contextual detection, including indoor and outdoor detection and floor detection, which takes advantage of multiple sensors integrated on Android, functional configuration, whose work is to establish and manage Wi-Fi fingerprint database. We developed an Android APP that runs on the smartphones and experiments have been carried out in real environment with good accuracy of output. Experimental results indicate a remarkable performance improvement by using the proposed method about HMM.

An Optimized Fusion Method for Double-Wearable-Wireless-Band Platform on Cloud-Health Application

This paper presents a stable double-wireless-wearable-band platform that can detect hand gestures. The real-time monitoring and control system utilizes an MCU processor, a wireless transceiver, and a commercial three-axis, digital-output MEMS accelerometer. To detect the user’s hand movements, a 3D virtual environment is created via a double-wearable-band controller. Compared with a single wearable band, double wearable bands can identify more gestures with improved stability. Performances in terms of control and detection are discussed in detail. This research development allows the user to specify desired two-hand postures using the multi-sensor information fusion technique for controlling a variety of robotic devices. In the system, the defined two-hand postures also allow the user to add freestyle control to various applications, which bridge the communication gap between humans and the systems. Moreover, the integration of the action recognition algorithm of the combination of two bracelets and the server brings out a real-time approach to analyze and make decisions based on the users’ data. Therefore, the system can call for help in a timely manner under critical conditions.

An optimized fusion method for double-wearable-wireless-band platform on remote controller

This paper presents a stable double-wireless wearable-band platform that can detect hand gesture using a wearable wireless sensor platform with a digital accelerometer. The real-time monitoring and control system utilizes an MCU, a Bluetooth transceiver, and a commercial three-axis, digital output MEMS accelerometer. It is a fairly recent research development to allow the user to specify desired double hand postures for controlling a variety of robotic devices. Compared with a single wearable band, double wearable bands can identify more gestures with improved stability. To detect the users hand movements, a 3D virtual environment is created via a double wearable band controller. In the system, the defined two-hand postures also allow the user to add control elements to various applications, which bridge the gap between humans and the systems.