Reducing power and increasing accuracy of on-body sensing in motion capture application.

Motion capture coupled with on-body sensing and biofeedback are key enabling technologies for assisted motor rehabilitation. However, wearability, power efficiency and measurement repeatability remain the principle challenges that need to be addressed before widespread adoption of such systems becomes possible. The weight and the size of the on-body sensing system needs to be kept small, and the system should not interfere with the user's movements or actions, but in general they are bulky due to their power consumption requirements. Furthermore, on-body sensors are very sensitive to positioning, which causes increased variability in the motion data. Isolating the characteristic patterns that represent the most important motion data affected by random positioning errors, while also reducing the power consumption, is the authors' main concern. An automated computational approach is considered to address the two problems. The use of functional principal component analysis is investigated for signal separation, whilst accounting for variability in the sensor position. To generate motion data, movements of human subjects and a robot arm are captured. As joint angles are considered in the analysis, the results are independent from the technology used to measure motion. The proposed post-processing technique can compensate for uncertainties due to sensor positional changes, whilst allowing greater energy efficiency of the sensors, thus enabling improved flexibility and usability of on-body sensing

Zernike velocity moments for sequence-based description of moving features

The increasing interest in processing sequences of images motivates development of techniques for sequence-based object analysis and description. Accordingly, new velocity moments have been developed to allow a statistical description of both shape and associated motion through an image sequence. Through a generic framework motion information is determined using the established centralised moments, enabling statistical moments to be applied to motion based time series analysis. The translation invariant Cartesian velocity moments suffer from highly correlated descriptions due to their non-orthogonality. The new Zernike velocity moments overcome this by using orthogonal spatial descriptions through the proven orthogonal Zernike basis. Further, they are translation and scale invariant. To illustrate their benefits and application the Zernike velocity moments have been applied to gait recognition—an emergent biometric. Good recognition results have been achieved on multiple datasets using relatively few spatial and/or motion features and basic feature selection and classification techniques. The prime aim of this new technique is to allow the generation of statistical features which encode shape and motion information, with generic application capability. Applied performance analyses illustrate the properties of the Zernike velocity moments which exploit temporal correlation to improve a shape's description. It is demonstrated how the temporal correlation improves the performance of the descriptor under more generalised application scenarios, including reduced resolution imagery and occlusion

Living IoT: A Flying Wireless Platform on Live Insects

Sensor networks with devices capable of moving could enable applications ranging from precision irrigation to environmental sensing. Using mechanical drones to move sensors, however, severely limits operation time since flight time is limited by the energy density of current battery technology. We explore an alternative, biology-based solution: integrate sensing, computing and communication functionalities onto live flying insects to create a mobile IoT platform. Such an approach takes advantage of these tiny, highly efficient biological insects which are ubiquitous in many outdoor ecosystems, to essentially provide mobility for free. Doing so however requires addressing key technical challenges of power, size, weight and self-localization in order for the insects to perform location-dependent sensing operations as they carry our IoT payload through the environment. We develop and deploy our platform on bumblebees which includes backscatter communication, low-power self-localization hardware, sensors, and a power source. We show that our platform is capable of sensing, backscattering data at 1 kbps when the insects are back at the hive, and localizing itself up to distances of 80 m from the access points, all within a total weight budget of 102 mg.Comment: Co-primary authors: Vikram Iyer, Rajalakshmi Nandakumar, Anran Wang, In Proceedings of Mobicom. ACM, New York, NY, USA, 15 pages, 201