In this paper, we describe and validate the EquiMoves system, which aims to support equine veterinarians in assessing lameness and gait performance in horses. The system works by capturing horse motion from up to eight synchronized wireless inertial measurement units. It can be used in various equine gait modes, and analyzes both upper-body and limb movements. The validation against an optical motion capture system is based on a Bland–Altman analysis that illustrates the agreement between the two systems. The sagittal kinematic results (protraction, retraction, and sagittal range of motion) show limits of agreement of ±2.3 degrees and an absolute bias of 0.3 degrees in the worst case. The coronal kinematic results (adduction, abduction, and coronal range of motion) show limits of agreement of −8.8 and 8.1 degrees, and an absolute bias of 0.4 degrees in the worst case. The worse coronal kinematic results are most likely caused by the optical system setup (depth perception difficulty and suboptimal marker placement). The upper-body symmetry results show no significant bias in the agreement between the two systems; in most cases, the agreement is within ±5 mm. On a trial-level basis, the limits of agreement for withers and sacrum are within ±2 mm, meaning that the system can properly quantify motion asymmetry. Overall, the bias for all symmetry-related results is less than 1 mm, which is important for reproducibility and further comparison to other systems.

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, which can run on resource constrained devices. The tests with the implementation on real sensor nodes show that the method distinguishes between joint and separate movements. In addition, we analyse 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 accurate and more robust to sensor alignment problems.1. How to extract and communicate the movement information? 2. How to compute the correlation, taking into account the resource limitations of the sensor nodes? 3. How does the method scale with the number of nodes? 4. How accurate is the solution and which are the benefits and limitations?The contribution of this paper is a lightweight, fast and cheap method for correlating the movement data among sensor nodes, for the purpose of clustering nodes moving together. Each node correlates the movement data generated by the local movement sensor with the movement data broadcast periodically by its neighbours. The result of the correlation is a measure of the confidence that one node shares the same context with its neighbours, for example that they are placed in the same car. We focus in this paper on correlating sensor nodes carried by vehicles on wheels.We describe two possible practical solutions, one using tilt switches, and another one using MEMS accelerometers. In order to answer the aforementioned questions in detail, we analyse the scalability from several different perspectives (communication, energy, memory and execution speed), and discuss the most relevant advantages and limitations. The analysis is based on the experimental results obtained from testing with real sensor nodes. We use the Ambient µNode 2.0 platform [1] with the low-power MSP430 micro-controller produced by Texas Instruments, which offers 48kB of Flash memory and 10kB of RAM. The radio transceiver has a maximum data rate of 100kbps. Figure 1 shows the sensors used for extracting the movement information and the sensor node platform.

No abstract

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 results obtained from a series of experiments with nine subjects show that the motion sensors are within 2.2°to 6.4°from the reference given by the motion capture system, with a correlation coefficient above 0.9. The wireless characteristics of our system, the energy expenditure, possible improvements and usability aspects are further analysed and discussed.

We propose an energy-efficient service discovery protocol for heterogeneous wireless sensor networks. Our solution exploits a cluster overlay, where the clusterhead nodes form a distributed service registry. A service lookup results in visiting only the clusterhead nodes. We aim for minimizing the communication costs during discovery of services and maintenance of a functional distributed service registry. To achieve these objectives we propose a clustering algorithm which makes decisions based on 1-hop neighbourhood information, avoids chain reactions and constructs a set of sparsely distributed clusterheads. We analyse how the properties of the clustering structure influence the performance of the service discovery protocol, by comparing our proposed clustering algorithm with DMAC. We evaluate the performance and the tradeoffs between the cluster-based service discovery approaches and the traditional flood-based solutions. We investigate the level of network heterogeneity where clustering is feasible for implementation in a wireless sensor network. Our analysis shows that cluster-based solutions are best suited for heterogeneous dense networks, with limited dynamics.