A major limitation in nutritional science is the lack of understanding of the nutritional intake of free-living people. There is an inverse relationship between accuracy of reporting of energy intake by all current nutritional methodologies and body weight. In this pilot study we aim to explore whether using a novel lightweight, wearable micro-camera improves the accuracy of dietary intake assessment. Doubly labelled water (DLW) was used to estimate energy expenditure and intake over a 14-d period, over which time participants (n 6) completed a food diary and wore a micro-camera on 2 of the days. Comparisons were made between the estimated energy intake from the reported food diary alone and together with the images from the micro-camera recordings. There was an average daily deficit of 3912 kJ using food diaries to estimate energy intake compared with estimated energy expenditure from DLW (P=0·0118), representing an under-reporting rate of 34 %. Analysis of food diaries alone showed a significant deficit in estimated daily energy intake compared with estimated intake from food diary analysis with images from the micro-camera recordings (405 kJ). Use of the micro-camera images in conjunction with food diaries improves the accuracy of dietary assessment and provides valuable information on macronutrient intake and eating rate. There is a need to develop this recording technique to remove user and assessor bias.

Inspired by the behavior of humans talking in noisy environments, we propose an embodied embedded cognition approach to improve automatic speech recognition (ASR) systems for robots in challenging environments, such as with ego noise, using binaural sound source localization (SSL). The approach is verified by measuring the impact of SSL with a humanoid robot head on the performance of an ASR system. More specifically, a robot orients itself toward the angle where the signal-to-noise ratio (SNR) of speech is maximized for one microphone before doing an ASR task. First, a spiking neural network inspired by the midbrain auditory system based on our previous work is applied to calculate the sound signal angle. Then, a feedforward neural network is used to handle high levels of ego noise and reverberation in the signal. Finally, the sound signal is fed into an ASR system. For ASR, we use a system developed by our group and compare its performance with and without the support from SSL. We test our SSL and ASR systems on two humanoid platforms with different structural and material properties. With our approach we halve the sentence error rate with respect to the common downmixing of both channels. Surprisingly, the ASR performance is more than two times better when the angle between the humanoid head and the sound source allows sound waves to be reflected most intensely from the pinna to the ear microphone, rather than when sound waves arrive perpendicularly to the membrane.

Purpose: Compact master manipulators have inherent advantages, since they can have practical deployment within the general surgical environments easily and bring benefits to surgical training. To assess the advantages of compact master manipulators for surgical skills training and the performance of general robot-assisted surgical tasks, Hamlyn Compact Robotic Master (Hamlyn CRM) is built up and evaluated in this paper. Methods: A compact structure for the master manipulator is proposed. A novel sensing system is designed while stable real-time motion tracking can be realized by fusing the information from multiple sensors. User studies were conducted based on a ring transfer task and a needle passing task to explore a suitable mapping strategy for the compact master manipulator to control a surgical robot remotely. The overall usability of the Hamlyn CRM is verified based on the da Vinci Research Kit (dVRK). The master manipulators of the dVRK control console are used as the reference Results: Motion tracking experiments verified that the proposed system can track the operators’ hand motion precisely. As for the master–slave mapping strategy, user studies proved that the combination of the position relative mapping mode and the orientation absolute mapping mode is suitable for Robot-Assisted Minimally Invasive Surgery (RAMIS), while key parameters for mapping are selected. Conclusion: Results indicated that the Hamlyn CRM can serve as a compact master manipulator for surgical training and has potential applications for RAMIS.

Master-Slave control is user interaction for Robot-Assisted Minimally Invasive Surgery (RAMIS). The configuration, as well as the placement of the master manipulators, can influence the remote control performance. An ergonomic shared workspace analysis framework is proposed in this paper. Combined with the workspace of the master manipulators and the human arms, the human-robot interaction workspace can be generated. The optimal master robot placement can be determined based on three criteria: 1) interaction workspace volume, 2) interaction workspace quality, and 3) intuitiveness for slave robot control. Experimental verification of the platform is conducted on a da Vinci Research Kit (dVRK). An in-house compact master manipulator (Hamlyn CRM) is used as the master robot and the da Vinci robot is used as the slave robot. Comparisons are made between with and without using design optimization to validate the effectiveness of the ergonomic shared workspace analysis technique. Results indicate that the proposed ergonomic shared workspace analysis can improve the performance of teleoperation in terms of task completion time and the number of clutching required during operation.