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.

Abstract. 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.

SummaryBackgroundInertial measurement unit (IMU) sensor‐based techniques are becoming more popular in horses as a tool for objective locomotor assessment.ObjectivesTo describe, evaluate and validate a method of stride detection and quantification at walk and trot using distal limb mounted IMU sensors.Study designProspective validation study comparing IMU sensors and motion capture with force plate data.MethodsA total of seven Warmblood horses equipped with metacarpal/metatarsal IMU sensors and reflective markers for motion capture were hand walked and trotted over a force plate. Using four custom built algorithms hoof‐on/hoof‐off timing over the force plate were calculated for each trial from the IMU data. Accuracy of the computed parameters was calculated as the mean difference in milliseconds between the IMU or motion capture generated data and the data from the force plate, precision as the s.d. of these differences and percentage of error with accuracy of the calculated parameter as a percentage of the force plate stance duration.ResultsAccuracy, precision and percentage of error of the best performing IMU algorithm for stance duration at walk were 28.5, 31.6 ms and 3.7% for the forelimbs and −5.5, 20.1 ms and −0.8% for the hindlimbs, respectively. At trot the best performing algorithm achieved accuracy, precision and percentage of error of −27.6/8.8 ms/−8.4% for the forelimbs and 6.3/33.5 ms/9.1% for the hindlimbs.Main limitationsThe described algorithms have not been assessed on different surfaces.ConclusionsInertial measurement unit technology can be used to determine temporal kinematic stride variables at walk and trot justifying its use in gait and performance analysis. However, precision of the method may not be sufficient to detect all possible lameness‐related changes. These data seem promising enough to warrant further research to evaluate whether this approach will be useful for appraising the majority of clinically relevant gait changes encountered in practice.

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.

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