Ketamine is an addictive drug that has been shown to inflict considerable physical and mental damage on users. Due in part to its low cost, ketamine has become one of the most popular club drugs among young adults and teenagers in Southeast Asia. This paper proposes a phone-based support system (KeDiary) with Bluetooth-enabled device for the screening of saliva, as a means of assisting ketamine-dependent patients to self-monitor their ketamine use following acute withdrawal treatment. We also conducted a practical experiment to evaluate the feasibility of the proposed system, wherein three ketamine-dependent patients self-administered tests at least once per day over a period of three weeks. Follow-up interviews with the same users helped in the further refinement of the proposed self-monitoring system.

Alcohol dependence is a chronic disorder associated with severe harm in multiple areas, and relapsing is easy, despite treatment. This study proposes SoberDiary, a phone-based support system that enables alcohol-dependent patients to self-monitor and -manage their own alcohol behavior, and remain sober in their daily lives. We tested SoberDiary in a real-life 12-week user study involving 27 clinical patients. The quantitative and qualitative results revealed that SoberDiary helped patients self-monitor and -manage their alcohol-use behavior, and reduced their total alcohol consumption as well as the number of heavy drinking days. Compared with patients who received standard treatment alone, this study demonstrated SoberDiary successfully complemented current alcohol treatment in reducing patients' alcoholic cravings and dropout rate over 3-month study period. Follow-up interviews further revealed the sophisticated use practices and value of SoberDiary.

This paper describes the design of a digital fork and a mobile interactive and persuasive game for a young child who is a picky eater and/or easily distracted during mealtime. The system employs Ubicomp technology to educate children on the importance of a balanced diet while motivating proper eating behavior. To sense a child's eating behavior, we have designed and prototyped a sensor-embedded digital fork, called the Sensing Fork. Furthermore, we have developed a story-book and persuasive game, called the Hungry Panda, on a smartphone. This capitalizes on the capabilities of the Sensing Fork to interact with and modify children's eating behavior during mealtime. We report the results of a real-life study that involves mother-child subjects and tested the effectiveness of the Sensing Fork and Hungry Panda game in addressing children's eating problems. Our findings exhibit positive effects for changing children's eating behavior.

To facilitate the collection of patient biosignals, designing extensible sensing devices in which sensor management is simplified is essential. This paper presents BioScope, an extensible sensing system that facilitates collecting data used in nursing assessments. We conducted experiments to demonstrate the potential of the system. The results obtained in this study can be applied in improving the design, thus enabling BioScope to facilitate data collection in numerous potential applications.

The effective end-to-end transport of delay-sensitive voice data has long been a problem in multimedia networking. One of the major issues is determining the sending rate of real-time VoIP streams such that the user experience is maximized per unit network resource consumed. A particularly interesting complication that remains to be addressed is that the available bandwidth is often dynamic. Thus, it is unclear whether a marginal increase warrants better user experience. If a user naively tunes the sending rate to the optimum at any given opportunity, the user experience could fluctuate. To investigate the effects of magnitude and frequency of rate changes on user experience, we recruited 127 human participants to systematically score emulated Skype calls with different combinations of rate changes, including varying magnitude and frequency of rate changes. Results show that: 1) the rate change frequency affects the user experience on a logarithmic scale, echoing Weber-Fechner's Law; 2) the effect of rate change magnitude depends on how users perceive the quality difference; and 3) this study derives a closed-form model of user perception for rate changes for Skype calls.

As Skype has become popular and a profitable business, the long-standing problem of how to deliver Skype calls deserves a serious revisit from an economic viewpoint. This study proposes a rate control mechanism for Skype calls that satisfies more users and satisfies users more than the greedy-naïve mechanism, as well as the mechanism implemented in Skype.

This study presents several extensions to our previous work on the PipeProbe system, a mobile sensor system for identifying the spatial topology of hidden water pipelines (i.e., non-moldable pipes such as copper and PVC) behind walls or under floors [Lai et al. 2010]. The PipeProbe system works by dropping a tiny wireless sensor capsule into the source of a water pipeline. As the PipeProbe capsule traverses the pipelines, it gathers and transmits pressure and angular velocity readings. Through spatiotemporal analysis of these sensor readings, the proposed algorithm locates all turning points in the pipelines and maps their 3D spatial topology. This study expands upon previous research by developing new sensing techniques that identify variable-diameter pipes and differentiate 90-degree pipe turns from 45-degree pipe bends.

This paper proposes Listen-to-Nose, a phone-based system that detects and records when and where a person's nose-related symptoms, such as sneezing and runny nose, occur in everyday settings. It is hoped that this system can be used to collect reference data for doctors to diagnose the cause of these symptoms.

This study proposes a phone-based system to detect the gait anomalies of a person waking under the influence of alcohol. This phone-based system can sense a person's alcohol usage and record the location/time context. This data can help identify problem drinking behaviors, such as drinking before work or before driving.

This study presents TriopusNet, a mobile wireless sensor network system for autonomous sensor deployment in pipeline monitoring. TriopusNet works by automatically releasing sensor nodes from a centralized repository located at the source of the water pipeline. During automated deployment, TriopusNet runs a sensor deploy-ment algorithm to determine node placement. While a node is flowing inside the pipeline, it performs placement by extending its mechanical arms to latch itself onto the pipe's inner surface. By continuously releasing nodes into pipes, the TriopusNet system builds a wireless network of interconnected sensor nodes. When a node runs at a low battery level or experiences a fault, the TriopusNet system releases a fresh node from the repository and performs a node replacement algorithm to replace the failed node with the fresh one. We have evaluated the TriopusNet system by creating and collecting real data from an experimental pipeline testbed. Comparing with the non-automated static deployment, TriopusNet is able to use less sensor nodes to cover a sensing area in the pipes while maintaining network connectivity among nodes with high data collection rate. Experimental results also show that TriopusNet can recover from the network disconnection caused by a battery-depleted node and successfully replace the battery-depleted node with a fresh node.

This paper proposes a blimp-based vehicular sensing system, called BlimpProbe, which travels above ground to perform environmental monitoring. We describe an application in atmosphere science that is suitable for this blimp-based sensing system.

The effective end-to-end transport of delay-sensitive voice data has long been a problem in multimedia networking. One of the major issues is determining the sending rate of real-time VoIP streams such that the user experience is maximized per unit network resource consumed. A particularly interesting complication that remains to be addressed is that the available bandwidth is often dynamic. Thus, it is unclear whether a marginal increase warrants better user experience. If a user naively tunes the sending rate to the optimum at any given opportunity, the user experience could fluctuate.

To promote energy-saving behavior, disaggregating electricity usage is critical for increasing consumer awareness of energy usage behavior. This study proposes HeatProbe, a thermal-based power meter system that uses thermal imaging to track disaggregated appliance usage. We have designed, prototyped, and tested the HeatProbe system. Results show that HeatProbe successfully senses individual appliance operating durations with an average error of 125.03 seconds, achieving 80.2% appliance power accounting accuracy in different appliance usage scenarios.

Saving DTNs' battery power to prolong the system life time has been an important research topic which scientists try to optimize. Most researches fall into two categories - Time Division Multiple Access based ones, and Low Power Listening ones by turning the listener's radio on/off intermittenly with long sending preambles. In this paper we propose solving the problem from a whole new angle - by using the motes' physical behavior, which is detectable by relatively energy-cheap accelerometer, as the judgment for turning the radio on/off. This scheme can be used in combination with any energy efficient DTN MAC protocols. We evaluate the protocol by running simulations with real hikers' traces and the result is shown in the evaluation section.

This paper presents PipeProbe, a mobile sensor system for determining the spatial topology of hidden water pipelines behind walls. PipeProbe works by dropping a tiny wireless sensor capsule into the source of the water pipelines. As the PipeProbe capsule traverses the pipelines, it gathers and transmits pressure and angular velocity readings. Through spatio-temporal analysis on the sensor readings, our algorithm locates all turning points in the pipelines and maps their 3D spatial topology. We evaluated the PipeProbe system by developing a prototype and using data collected in our experimental testbed. Results show that the PipeProbe system successfully located and estimated 90% of all pipe tube lengths within 8-cm accuracy on average tube lengths of 76 cm. PipeProbe also successfully located 90% of all turning points within 15-cm accuracy on average length paths of 335cm.

Wi-Fi ingerprint-based localization is currently the most promising approach to building metropolitan-scale localization systems for both indoor and outdoor environments. A Wi-Fi fingerprint localization system makes use of a radio map that contains Wi-Fi signals received at various locations, and uses the map for location estimation. Thus, the accuracy of a Wi-Fi fingerprint localization system greatly depends on the quality of its radio map. This paper explores various properties of a metropolitan radio map that may affect the accuracy of Wi-Fi fingerprint localization systems. In this study, metropolitan-scale radio maps are obtained using war walking and war driving. These maps contain hundreds of thousands of access points and signal samples. A detailed comparison analysis of selected radio map properties reveals how different map properties affect the difference between the positional accuracies of the driving and walking radio maps in Wi-Fi fingerprint-based localization.

In this paper we propose a new video interaction model called adaptive fast-forwarding to help people quickly browse videos with predefined semantic rules. This model is designed around the metaphor of scenic car driving, in which the driver slows down near areas of interest and speeds through unexciting areas. Results from a preliminary user study of our video player suggest the following: (1) the player should adaptively adjust the current playback speed based on the complexity of the present scene and predefined semantic events; (2) the player should learn user preferences about predefined event types as well as a suitable playback speed; (3) the player should fast-forward the video continuously with a playback rate acceptable to the user to avoid missing any undefined events or areas of interest. Furthermore, our user study results suggest that for certain types of video, our SmartPlayer yields better user experiences in browsing and fast-forwarding videos than existing video players' interaction models.

This study of mobile persuasion system explores the use of a mobile phone, when attached to an everyday object used by an everyday behavior, becomes a tool to sense and influence that behavior. This mobile persuasion system, called Playful Bottle system, makes use of a mobile phone attached to an everyday drinking mug and motivates office workers to drink healthy quantities of water. A camera and accelerometer sensors in the phone are used to build a vision/motion-based water intake tracker to detect the amount and regularity of water consumed by the user. Additionally, the phone includes hydration games in which natural drinking actions are used as game input. Two hydration games are developed: a single-user TreeGame with automated computer reminders and a multi-user ForestGame with computer-mediated social reminders from members of the group playing the game. Results from 7-week user study with 16 test subjects suggest that both hydration games are effective for encouraging adequate and regular water intake by users. Additionally, results of this study suggest that adding social reminders to the hydration game is more effective than system reminders alone.

We propose PipeProbe, a mobile sensor system for mapping water pipelines hidden inside cement walls or under floor coverings. PipeProbe works by dropping a tiny sensor capsule into the source of the water pipelines. As the PipeProbe capsule traverses the pipelines, it gathers accelerometer and water pressure readings. Through a novel estimation method based on Bernoulli's principle, we reconstruct the 3D spatial layout of the water pipelines. The PipeProbe system is non-intrusive and requires no alteration to the water pipeline infrastructure.

We propose PipeProbe, a mobile sensor system for mapping hidden water pipelines inside cement walls or under floor coverings. PipeProbe works by dropping a sensor capsule into the source of the water pipelines. As the PipeProbe capsule traverses the pipelines, it gathers water pressure and accelerometer readings. Given the pressure sensor readings, we can identify the height of PipeProbe. Accelerometer is used to recognize different types of water pipeline shape when PipeProbe travels through. The PipeProbe system is non-intrusive and requires no alteration to the water pipeline infrastructure.

Boundary detection is a form of location-aware services that aims at detecting targets crossing certain critical regions. Typically, a lower location sampling rate contributes to a lower level of energy consumption but, in the meantime, delays the detection of boundary crossing events. Opting to enable energy-efficient boundary detection services, we propose a mobility-aware mechanism that adapts the location sampling rate to the target mobility. Results from our simulations and live experiments confirm that the proposed adaptive sampling mechanism is effective. In particular, when experimented with realistic errors measured from a live radio-frequency-based localization system, the energy consumption can be reduced significantly to 20 percent.

This case study in UbiComp technology and design presents a system for assisting parents and teachers to motivate kindergarten children to learn proper and thorough brushing skills. The system includes a vision-based motion tracker that recognizes different tooth brushing strokes and a tooth brushing game in which the child cleans a virtual, mirror picture of his/her dirty teeth by physically brushing his/her own teeth. The user study results suggest that Playful Toothbrush enhances the effectiveness of kindergarten children in brushing their teeth, as measured by number of brushing strokes, duration of brushing and thoroughness of teeth cleaning.

This work proposes the novel use of spinning beacons for precise indoor localization. The proposed "SpinLoc" (Spinning Indoor Localization) system uses "spinning" (i.e., rotating) beacons to create and detect predictable and highly distinguishable Doppler signals for sub-meter localization accuracy. The system analyzes Doppler frequency shifts of signals from spinning beacons, which are then used to calculate orientation angles to a target. By obtaining orientation angles from two or more beacons, SpinLoc can precisely locate stationary or slow-moving targets. After designing and implementing the system using MICA2 motes, its performance was tested in an indoor garage environment. The experimental results revealed a median error of 40~50 centimeters and a 90% error of 70~90 centimeters.

In this paper, we address the challenges of content transfer in opportunistic networks, and propose techniques to better facilitate data dissemination based on the characteristics of the content. To investigate this problem from its origins, we propose three message scheduling algorithms: Sequential Forwarding (SF), Full Interleaving (FI), and Block-based Interleaving (BI). Each algorithm is embedded in a specially tailored data dissemination technique to evaluate the benefits of applying it to different types of content and data dissemination methods. Three types of content (file, video and web) are considered and evaluated, and the dissemination methods considered are Layered Multiple Description Coding (LMDC) based and file-based. Using simulations as well as both synthetic and realistic network scenarios, we evaluate the proposed schemes in terms of latency and user perceived quality, and demonstrate how the schemes can achieve much better latency performance for file transfers. Furthermore, we show that using LMDC-based techniques leads to higher user perceived quality, since the end user is allowed to "preview" video file or web content, even before the data has been completely transferred. The effectiveness and robustness of our message scheduling algorithms and their corresponding content dissemination techniques make them ideal solutions that can go a long way toward effective data dissemination in opportunistic networks.

Opportunistic network is a type of delay tolerant networks (DTN) where network communication opportunities appear opportunistic. In this study, we investigate opportunistic network scenarios based on public network traces, and our contributions are the following: First, we identify the censorship issue in network traces that usually leads to strongly skewed distribution of the measurements. Based on this knowledge, we then apply the Kaplan-Meier Estimator to calculate the survivorship of network measurements, which is used in designing our proposed censorship removal algorithm (CRA) that is used to recover censored data. Second, we perform a rich set of analysis illustrating that UCSD and Dartmouth network traces show strong self-similarity, and can be modeled as such. Third, we pointed out the importance of these newly revealed characteristics in future development and evaluation of opportunistic networks.

Energy efficiency and positional accuracy are often contradictive goals. We propose to decrease power consumption without sacrificing significant accuracy by developing an energy-aware localization that adapts the sampling rate to target's mobility level. In this paper, an energy-aware adaptive localization system based on signal strength fingerprinting is designed, implemented, and evaluated. Promising to satisfy an application's requirements on positional accuracy, our system tries to adapt its sampling rate to reduce its energy consumption. The contribution of this paper is three-fold. (1) We have developed a model to predict the positional error of a real working positioning engine under different mobility levels of mobile targets, estimation error from the positioning engine, processing and networking delay in the location infrastructure, and sampling rate of location information. (2) In a real test environment, our energy-saving method solves the mobility estimation error problem by utilizing additional sensors on mobile targets. The result is that we can improve the prediction accuracy by as much as 37.01%. (3) We implemented our energy-saving methods inside a working localization infrastructure and conducted performance evaluation in a real office environment. Our performance results show as much as 49.76 % reduction in power consumption

Wi-Fi based indoor location systems have been shown to be both cost-effective and accurate, since they can attain meter-level positioning accuracy by using existing Wi-Fi infrastructure in the environment. However, two major technical challenges persist for current Wi-Fi based location systems, instability in positioning accuracy due to changing environmental dynamics, and the need for manual offline calibration during site survey. To address these two challenges, three environmental factors (people, doors, and humidity) that can interfere with radio signals and cause positioning inaccuracy are identified. Then, we have proposed a sensor-assisted adaptation method that employs RFID sensors and environment sensors to adapt the location systems automatically to the changing environmental dynamics. The proposed adaptation method performs online calibration to build multiple context-aware radio maps under various environmental conditions. Experiments were performed on the sensor-assisted adaptation method. The experimental results show that the proposed adaptive method can avoid adverse reduction in positioning accuracy under changing environmental dynamics.

The work presents the mProducer tool that enables multimedia content authoring on mobile phones. It addresses three major challenges: limited storage space on mobile phones; costly and slow wireless links; limited mobile user attention. Our contribution is twofold: (1) a novel technique called storage constrained off-loading (SCO) that intelligently uploads selected portions of contents to a remote storage server so that the amount of contents a user can capture is not restricted by the size of the small mobile storage; (2) two new UI techniques, the keyframe based editing and automatically generated content navigation tree, to reduce the amount of user efforts for editing.

Personal experience computing is about computing support for recording, storing, retrieving, editing, analyzing, and sharing of personal experiences. In this paper, we present our design, implementation and evaluation of a mobile authoring tool called mProducer. mProducer enables a user to generate personal experience content using a mobile device anytime, anywhere. To address challenges in both limited system resources and user interface constraints on a mobile device, mProducer provides several innovative system techniques and UI designs. (1) The Storage Constrained Uploading (SCU) algorithm uploads large multimedia data to remote servers, in order to alleviate the problem of limited storage on a mobile device. (2) Sensor-Assisted Automated Editing utilizes a tilt sensor on the mobile device to automate the detection and removal of blurry frames resulting from excessive amount of camera shaking. This sensor-based solution requires small processing overhead, and it is considered a good alternative to computational-expensive image processing techniques for detecting shaking artifacts. (3) Map-based content management interface incorporates a GPS receiver on a mobile device to record location meta-data for each recording captured by a user, and enables easy, intuitive content navigation on a small screen. (4) Keyframe-based editing enables a user to edit content using only keyframes. We have conducted user studies to evaluate overall editing experience, user satisfaction in the editing quality, task performance time, ease-of-use, and learnability. The results of user studies have shown that keyframe-based editing works best with a storyboard interface. In general, users have found mProducer to be both fun and easy to use on a mobile device.