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Smart camera networks have recently emerged as a new class of sensor network infrastructure that is capable of supporting high-power in-network signal processing and enabling a wide range of applications. In this article, we provide an exposition of our efforts to build a low-bandwidth wireless camera network platform, called CITRIC, and its applications in smart camera networks. The platform integrates a camera, a microphone, a frequency-scalable (up to 624 MHz) CPU, 16 MB FLASH, and 64 MB RAM onto a single device. The device then connects with a standard sensor network mote to form a wireless camera mote. With reasonably low power consumption and extensive algorithmic libraries running on a decent operating system that is easy to program, CITRIC is ideal for research and applications in distributed image and video processing. Its capabilities of in-network image processing also reduce communication requirements, which has been high in other existing camera networks with centralized processing. Furthermore, the mote easily integrates with other low-bandwidth sensor networks via the IEEE 802.15.4 protocol. To justify the utility of CITRIC, we present several representative applications. In particular, concrete research results will be demonstrated in two areas, namely, distributed coverage hole identification and distributed object recognition.

Background Goniometers are commonly used by physical therapists to measure range-of-motion (ROM) in the musculoskeletal system. These measurements are used to assist in diagnosis and to help monitor treatment efficacy. With newly emerging technologies, smartphone-based applications are being explored for measuring joint angles and movement. Objective This pilot study investigates the intra- and inter-rater reliability as well as concurrent validity of a newly-developed smartphone magnetometer-based goniometer (MG) application for measuring passive shoulder abduction in both sitting and supine positions, and compare against the traditional universal goniometer (UG). Design This is a comparative study with repeated measurement design. Methods Three physical therapists utilized both the smartphone MG and a traditional UG to measure various angles of passive shoulder abduction in a healthy subject, whose shoulder was positioned in eight different positions with pre-determined degree of abduction while seated or supine. Each therapist was blinded to the measured angles. Concordance correlation coefficients (CCCs), Bland–Altman plotting methods, and Analysis of Variance (ANOVA) were used for statistical analyses. Results Both traditional UG and smartphone MG were reliable in repeated measures of standardized joint angle positions (average CCC > 0.997) with similar variability in both measurement tools (standard deviation (SD) ± 4°). Agreement between the UG and MG measurements was greater than 0.99 in all positions. Conclusion Our results show that the smartphone MG has equivalent reliability compared to the traditional UG when measuring passive shoulder abduction ROM. With concordant measures and comparable reliability to the UG, the newly developed MG application shows potential as a useful tool to assess joint angles.

Background: The concept of reachable workspace is closely tied to upper limb joint range of motion and functional capability. Currently, no practical and cost-effective methods are available in clinical and research settings to provide arm-function evaluation using an individual's three-dimensional (3D) reachable workspace. A method to intuitively display and effectively analyze reachable workspace would not only complement traditional upper limb functional assessments, but also provide an innovative approach to quantify and monitor upper limb function. Methodology/Principal Findings: A simple stereo camera-based reachable workspace acquisition system combined with customized 3D workspace analysis algorithm was developed and compared against a sub-millimeter motion capture system. The stereo camera-based system was robust, with minimal loss of data points, and with the average hand trajectory error of about 40 mm, which resulted to ~5% error of the total arm distance. As a proof-of-concept, a pilot study was undertaken with healthy individuals (n = 20) and a select group of patients with various neuromuscular diseases and varying degrees of shoulder girdle weakness (n = 9). The workspace envelope surface areas generated from the 3D hand trajectory captured by the stereo camera were compared. Normalization of acquired reachable workspace surface areas to the surface area of the unit hemi-sphere allowed comparison between subjects. The healthy group's relative surface areas were 0.618±0.09 and 0.552±0.092 (right and left), while the surface areas for the individuals with neuromuscular diseases ranged from 0.03 and 0.09 (the most severely affected individual) to 0.62 and 0.50 (very mildly affected individual). Neuromuscular patients with severe arm weakness demonstrated movement largely limited to the ipsilateral lower quadrant of their reachable workspace. Conclusions/Significance: The findings indicate that the proposed stereo camera-based reachable workspace analysis system is capable of distinguishing individuals with varying degrees of proximal upper limb functional impairments. © 2012 Kurillo et al.

We present a mobile platform for body sensor networking based on a smartphone for lightweight signal processing of sensor mote data. The platform allows for local processing of data at both the sensor mote and smartphone levels, reducing the overhead of data transmission to remote services. We discuss how the smartphone platform not only provides the ability for wearable signal processing, but it allows for opportunistic sensing strategies, in which many of the onboard sensors and capabilities of modern smartphones may be collected and fused with body sensor data to provide environmental and social context. We propose that this can help refine data reduction at the local level. We describe three examples related to health and wellness, to which our system has been applied.

WAVE is an API for Android OS which which allows for easy management of body sensor networks (BSNs) on mobile platforms. It presents a simple framework for health-oriented applications by providing functionality for data collection from sensors and data processing. CalFit is an interactive application built using WAVE that leverages the power of social influence to promote physical activity.

We present an application of an open source platform for wireless body sensor network called DexterNet to the problem of children's asthma. The architecture of the system consists of three layers. At the body sensor layer (BSL), the integrated monitoring of a child's activities, geographic location, and air pollution exposures occurs. At the personal network layer (PNL), a wireless mobile device worn by the child summarizes the sensed data, and provides information feedback. The mobile device communicates wirelessly over the Internet with the third global network layer (GNL), in which a web server provides the following four information services: a clinical module that supports the healthcare management of asthma cases, a personal health module that supports individual prevention of asthma attacks, a community module that supports participatory sensing, and a health research module that supports the collection of anonymous sensor data for research into the risk factors associated with asthma. We illustrate the potential for the system to serve as a comprehensive strategy to manage asthma cases and prevent asthma attacks.

We present an open-source platform for wireless body sensor networks called DexterNet. The system supports real-time, persistent human monitoring in both indoor and outdoor environments. The platform utilizes a three-layer architecture to control heterogeneous body sensors. The first layer called the body sensor layer (BSL) deals with design of heterogeneous body sensors and their instrumentation on the body. At the second layer called the personal network layer (PNL), the body sensors on a single subject communicate with a mobile base station, which supports Linux OS and the IEEE 802.15.4 protocol. The BSL and PNL functions are abstracted and implemented as an open-source software library, called Signal Processing In Node Environment (SPINE). A DexterNet network is scalable, and can be reconfigured on-the-fly via SPINE. At the third layer called the global network layer (GNL), multiple PNLs communicate with a remote Internet server to permanently log the sensor data and support higher-level applications. We demonstrate the versatility of the DexterNet platform via several real-world applications.

In this paper, we propose and demonstrate a novel wireless camera network system, called CITRIC. The core component of this system is a new hardware platform that integrates a camera, a frequency-scalable (up to 624 MHz) CPU, 16MB FLASH, and 64MB RAM onto a single device. The device then connects with a standard sensor network mote to form a camera mote. The design enables in-network processing of images to reduce communication requirements, which has traditionally been high in existing camera networks with centralized processing. We also propose a back-end client/server architecture to provide a user interface to the system and support further centralized processing for higher-level applications. Our camera mote enables a wider variety of distributed pattern recognition applications than traditional platforms because it provides more computing power and tighter integration of physical components while still consuming relatively little power. Furthermore, the mote easily integrates with existing low-bandwidth sensor networks because it can communicate over the IEEE 802.15.4 protocol with other sensor network platforms. We demonstrate our system on three applications: image compression, target tracking, and camera localization.