Understanding users' behavior at home is central to behavioral research. For example, social researchers are interested in studying domestic abuse, and healthcare professionals are interested in caregiver-patient interaction. Today, such studies rely on diaries and questionnaires, which are subjective, erroneous, and hard to sustain in longitudinal studies. We introduce Marko, a system that automatically collects behavior-related data, without asking people to write diaries or wear sensors. Marko transmits a low power wireless signal and analyses its reflections from the environment. It maps those reflections to how users interact with the environment (e.g., access to medication cabinet) and with each other (e.g., watch TV together). It provides novel algorithms for identifying who-does-what, and bootstrapping the system in new homes without asking users for new annotations. We evaluate Marko with a one-month deployment in six homes, and demonstrate its value for studying couple relationships and caregiver-patient interaction.

Gait velocity and stride length are critical health indicators for older adults. A decade of medical research shows that they provide a predictor of future falls, hospitalization, and functional decline among seniors. However, currently these metrics are measured only occasionally during medical visits. Such infrequent measurements hamper the opportunity to detect changes and intervene early in the impairment process.

We present RF-Capture, a system that captures the human figure -- i.e., a coarse skeleton -- through a wall. RF-Capture tracks the 3D positions of a person's limbs and body parts even when the person is fully occluded from its sensor, and does so without placing any markers on the subject's body. In designing RF-Capture, we built on recent advances in wireless research, which have shown that certain radio frequency (RF) signals can traverse walls and reflect off the human body, allowing for the detection of human motion through walls. In contrast to these past systems which abstract the entire human body as a single point and find the overall location of that point through walls, we show how we can reconstruct various human body parts and stitch them together to capture the human figure. We built a prototype of RF-Capture and tested it on 15 subjects. Our results show that the system can capture a representative human figure through walls and use it to distinguish between various users.

Abstract With the emergence of Internet of Things (IoT), trillions of devices will soon be interconnected and provide new services. The success of IoT relies on the scalability of underlying machine‐to‐machine communication networks. In this paper, we propose a distributed and adaptive multichannel protocol to address the scalability issue. The proposed protocol is composed of (i) real‐time estimation of competing devices, (ii) adaptive channel access probability, and (iii) asynchronous resource reservation. The three features ensure that channel utilization is maximized even when the number of competing devices and their traffic fluctuate dramatically. Our numerical and simulation results show that the proposed protocol can achieve a channel utilization of up to 93%, especially when the number of competing machine is large. Copyright © 2014 John Wiley & Sons, Ltd.

Light field photography, which has recently drawn considerable attention, provides novel functionalities such as refocusing and depth estimation at the same time. However, the resolution of the rendered refocus image is incomparably lower than the number of light ray samples in the light field. In this paper, we show that super-resolution can be performed to bridge the gap and that deconvolution, which is missing in most previous methods, is essential to the success of light field super-resolution rendering. In addition, we investigate the effect of four different kinds of camera calibration error on the quality of rendered images. Given an expected level of image quality, the upperbound of the camera calibration error is analyzed.

With the emergence of machine-to-machine (M2M) communications, trillions of devices will soon be interconnected to support new applications and services. The success of M2M communication relies on the scalability of underlying network architectures and protocols. In this paper, an adaptive multi-channel medium access control (MAC) protocol is proposed to address the scalability issue in M2M communications. The proposed MAC protocol enables devices to (1) real-time estimate the number of competing devices and (2) adjust their operation parameters to maximize channel utilization. Our numerical results show that the proposed protocol outperforms the existing multi-channel protocols, especially when the number of competing devices is large and fluctuates with time.