Ambulatory Estimation of Relative Foot Positions using Ultrasound

The recording of human movement is used for biomedical applications like physical therapy and sports training. Over the last few years inertial sensors have been proven to be a useful ambulatory alternative to traditional optical systems. An example of a successful application is the instrumented shoe, which contains two 6D force/moment sensors beneath the heel and the forefoot and two inertial sensors rigidly attached to the force/moment sensors [1]. These shoes can be used for ambulatory assessment of walking kinetics and kinematics. The relative position of the feet is currently not measured directly but estimated from double integration of feet accelerations. However, this method immediately leads to large position errors (drift) when the estimated inertial accelerations are inaccurate. In this study we investigated the ambulatory estimation of the relative positions of the feet using ultrasound transducers. On one shoe we mounted a 400PT120 Air Ultrasonic Ceramic Transducer (13 mm diameter, 10 mm height, 85º beam angle) sending a 40 kHz pulse to a similar transducer on the other shoe. Using the time of flight, the distance is estimated. Under static conditions a mean error of 5.7 ±0.8 mm was obtained over a range of 5 till 75 cm [2]. From this pilot study we concluded that the distance between the feet can be estimated ambulatory using small and low-cost ultrasound transducers. Future research includes the use of multiple transducers on each foot for a distance measure during different daily-life activities. Also the relative positions of the feet will be investigated by fusing the distance estimates with inertial sensor data

MACISH: Designing Approximate MAC Accelerators with Internal-Self-Healing

Approximate computing studies the quality-efficiency trade-off to attain a best-efficiency (e.g., area, latency, and power) design for a given quality constraint and vice versa. Recently, self-healing methodologies for approximate computing have emerged that showed an effective quality-efficiency tradeoff as compared to the conventional error-restricted approximate computing methodologies. However, state-of-the-art self-healing methodologies are constrained to highly parallel implementations with similar modules (or parts of a datapath) in multiples of two and for square-accumulate functions through the pairing of mirror versions to achieve error cancellation. In this article, we propose a novel methodology for InternalSelf-Healing (ISH) that allows exploiting self-healing within a computing element internally without requiring a paired, parallel module, which extends the applicability to irregular/asymmetric datapaths while relieving the restriction of multiples of two for modules in a given datapath, as well as going beyond square functions. We employ our ISH methodology to design an approximate multiply-accumulate (xMAC), wherein the multiplier is regarded as an approximation stage and the accumulator as a healing stage. We propose to approximate a recursive multiplier in such a way that a near-to-zero average error is achieved for a given input distribution to cancel out the error at an accurate accumulation stage. To increase the efficacy of such a multiplier, we propose a novel 2 × 2 approximate multiplier design that alleviates the overflow problem within an n × n approximate recursive multiplier. The proposed ISH methodology shows a more effective quality-efficiency trade-off for an xMAC as compared to the conventional error-restricted methodologies for random inputs and for radio-astronomy calibration processing (up to 55% better quality output for equivalent-efficiency designs)