Motion analysis systems measure and calculate the position of markers fixed to the body but generally restrict measurement to the laboratory environment. In contrast, inertial measurement devices are small, lightweight and self-contained and data collection is not restricted to a laboratory. Most research using inertial measurement in human locomotion studies has focused on walking. This paper describes a wireless accelerometer-based method for measuring shank angular velocity during sprint running. The system consists of body-mounted electronics with a wireless connection to a PC programmed with the necessary equations to interpret the acceleration data. The hardware incorporates two sets of accelerometers measuring acceleration in each of the three axes. The two 3D accelerometers are fixed to a frame so that their axes are aligned and they are separated by a prescribed distance. By calculating the difference in acceleration between the two 3D sensors, the gravitational component and linear acceleration components are cancelled leaving the rotational acceleration components. An onboard microcontroller digitises the accelerometer signals and the data is transmitted wirelessly to a PC to calculate the angular velocity with minimal latency. Tests were conducted on several subjects running at a constant velocity for several different speeds. The angular rate output from the accelerometer-based system was compared to data obtained from an optical motion analysis system. Validation test results indicate an accurate result was obtained. The design's suitability for acquiring data during elite athlete sprint training is examined and other applications considered. Error reduction strategies will also be discussed.
A biomechanical variable of interest to sprint coaches is foot-ground contact time. Contact time can be easily measured in a laboratory environment using a force platform, but is difficult to measure in the field. The focus of this paper is on the development and validation of an accelerometer-based method for estimating contact time during sprinting that could be used in the field. Tri-axial accelerometers were mounted on the tibia of the right leg of 6 subjects who performed maximal running trials from a stationary start, and running trials at a range of steady state speeds (jog, run and sprint). Ground contact times were measured using a force platform, and estimated from 3D accelerometer data. The mean error
between the force plate and accelerometer-based measures of contact time were 0 ± 12 ms, 2 ± 3 ms, and 1 ± 1 ms for the jog, run and sprint. For steps 1, 3 and 5 of the acceleration phase of the maximal sprint the mean errors were 8 ± 9 ms, 2 ± 5 ms, and 0 ± 1 ms respectively. Overall it was concluded from our analysis that close estimates of contact time during running can be obtained using body mounted accelerometers, with the best estimates obtained in conditions associated with the highest accelerations.
The use of environmental sensors in agriculture and precision agriculture applications is becoming more common, although implementation strategies and capital costs prohibit widespread adoption by many in the industry. Typical costs for agricultural monitoring systems can be in the tens of thousands of dollars per site. This paper presents low cost, wireless sensor nodes and a corresponding low power network. The nodes use biodegradable plastic to house the sensor, support electronics, RF transceiver and a 433 MHz antenna. In this paper the antenna design and network topology is discussed together with the propagation problems associated with a field environment in which the vegetation changes weekly. It is envisaged that such a platform could be ploughed in to the field at the end of its working life. The total cost of construction of the prototype platform is approximately $US10 per sensor. A communication protocol was also developed to allow many of these devices to be installed simultaneously and for the transmission of collected data and dynamic configuration and reprogramming. A receiver system allows for the collation and presentation of collected data. Low cost soil moisture sensors were coupled to the platform and installed in a commercial nursery wholesaler. Field trials of the network were successfully conducted.