Movement-based Group Awareness with Wireless Sensor Networks

We propose a method through which dynamic sensor nodes determine that they move together, by communicating and correlating their movement information. We describe two possible solutions, one using inexpensive tilt switches, and another one using low-cost MEMS accelerometers. We implement a fast, incremental correlation algorithm, with an execution time of 6ms, which can run on resource constrained devices. The tests with the implementation on real sensor nodes show that the method is reliable and distinguishes between joint and separate movements. In addition, we analyze the scalability from four different perspectives: communication, energy, memory and execution speed. The solution using tilt switches proves to be simpler, cheaper and more energy efficient, while the accelerometer-based solution is more reliable, more robust to sensor alignment problems and, potentially, more accurate by using extended features, such as speed and distance

Online Movement Correlation of Wireless Sensor Nodes

Sensor nodes can autonomously form ad-hoc groups based on their common context. We propose a solution for grouping sensor nodes attached on the same vehicles on wheels. The nodes periodically receive the movement data from their neighbours and calculate the correlation coefficients over a time history. A high correlation coefficient implies that the nodes are moving together. We demonstrate the algorithm using two types of movement sensors: tilt switches and MEMS accelerometers. We place the nodes on two wirelessly controlled toy cars, and we observe in real-time the group membership via the LED colours of the nodes. In addition, a graphical user interface running on the base station shows the movement signals over a recent time history, the latest sampled data, the correlation between each two nodes and the group membership

SensorShoe: Mobile Gait Analysis for Parkinson's Disease Patients

We present the design and initial evaluation of a mobile gait analysis system, SensorShoe. The target user group is represented by Parkinson's Disease patients, which need continuous assistance with the physical therapy in their home environment. SensorShoe analyses the gait by using a low-power sensor node equipped with movement sensors. In addition, SensorShoe gives real-time feedback and therapy assistance to the patient, and provides the caregivers an effective remote monitoring and control tool

Event Detection in Wireless Sensor Networks – Can Fuzzy Values Be Accurate?

Abstract. Event detection is a central component in numerous wireless sensor network (WSN) applications. In spite of this, the area of event description has not received enough attention. The majority of current event description approaches rely on using precise values to specify event thresholds. However, we believe that crisp values cannot adequately handle the often imprecise sensor readings. In this paper we demonstrate that using fuzzy values instead of crisp ones significantly improves the accuracy of event detection. We also show that our fuzzy logic approach provides higher detection precision than a couple of well established classification algorithms. A disadvantage of using fuzzy logic is the exponentially growing size of the rule-base. Sensor nodes have limited memory and storing large rulebases could be a challenge. To address this issue we have developed a number of techniques that help reduce the size of the rule-base by more than 70 % while preserving the level of event detection accuracy. Key words: wireless sensor networks, fuzzy logic, event description, event detection accuracy

D-FLER - A Distributed Fuzzy Logic Engine for Rule-Based Wireless Sensor Networks

We propose D-FLER, a distributed, general-purpose reasoning engine for WSN. D-FLER uses fuzzy logic for fusing individual and neighborhood observations, in order to produce a more accurate and reliable result. Thorough simulation, we evaluate D-FLER in a fire-detection scenario, using both fire and non-fire input data. D-FLER achieves better detection times, while reducing the false alarm rate. In addition, we implement D-FLER on real sensor nodes and analyze the memory overhead, the numerical accuracy and the execution time

A performance analysis of a wireless body-area network monitoring system for professional cycling

It is essential for any highly trained cyclist to optimize his pedalling movement in order to maximize the performance and minimize the risk of injuries. Current techniques rely on bicycle fitting and off-line laboratory measurements. These techniques do not allow the assessment of the kinematics of the cyclist during training and competition, when fatigue may alter the ability of the cyclist to apply forces to the pedals and thus induce maladaptive joint loading. We propose a radically different approach that focuses on determining the actual status of the cyclist’s lower limb segments in real-time and real-life conditions. Our solution is based on body area wireless motion sensor nodes that can collaboratively process the sensory information and provide the cyclists with immediate feedback about their pedalling movement. In this paper, we present a thorough study of the accuracy of our system with respect to the gold standard motion capture system. We measure the knee and ankle angles, which influence the performance as well as the risk of overuse injuries during cycling. The wireless characteristics of our system, the energy expenditure, possible improvements and usability aspects are analysed and discussed