Towards automation of dynamic-gaze video analysis taking functional upper-limb tasks as a case study.

BACKGROUND AND OBJECTIVE: Previous studies in motor control have yielded clear evidence that gaze behavior (where someone looks) quantifies the attention paid to perform actions. However, eliciting clinically meaningful results from the gaze data has been done manually, rendering it incredibly tedious, time-consuming, and highly subjective. This paper aims to study the feasibility of automating the coding process of the gaze data taking functional upper-limb tasks as a case study. METHODS: This is achieved by developing a new algorithm capable of coding the collected gaze data through three main stages; data preparation, data processing, and output generation. The input data in the form of a crosshair and a gaze video are converted into a 25 Hz frame rate sequence. Keyframes and non-key frames are then obtained and processed using a combination of image processing techniques and a fuzzy logic controller. In each trial, the location and duration of gaze fixation at the areas of interest (AOIs) are obtained. Once the gaze data is coded, it can be presented in different forms and formats, including the stacked color bar. RESULTS: The obtained results showed that the developed coding algorithm highly agrees with the manual coding method but significantly faster and less prone to unsystematic errors. Statistical analysis showed that Cohen's Kappa ranges from 0.705 to 1.0. Moreover, based on the intra-class correlation coefficient (ICC), the agreement index between computerized and manual coding methods is found to be (i) 0.908 with 95% confidence intervals (0.867, 0.937) for the anatomical hand and (ii) 0.923 with 95% confidence intervals (0.888, 0.948) for the prosthetic hand. A Bland-Altman plot also showed that all data points are closely scattered around the mean. These findings confirm the validity and effectiveness of the developed coding algorithm. CONCLUSION: The developed algorithm demonstrated that it is feasible to automate the coding of the gaze data, reduce the coding time, and improve the coding process's reliability.

Modular design of fatigue detection in naturalistic driving environments.

Research in driver mental fatigue is motivated by the fact that errors made by drivers often have life-threatening consequences. This paper proposes a new modular design approach for the early detection of driver fatigue system taking into account optimisation of system performance using particle swarm optimisation (PSO). The proposed system is designed and implemented using an existing dataset that was simultaneously collected from participants and vehicles in a naturalistic environment. Four types of data are considered as fatigue-related metrics including: vehicle acceleration, vehicle rotation pattern, driver's head position and driver's head rotation. The driver's blink rate data is used in this work as a proxy for ground truth for the classification algorithm. The collected data elements are initially fed to input modules represented by ternary neural network classifiers that estimates alertness. A Bayesian algorithm with PSO is then used to combine and optimise detection performance based on the number of existing input modules as well as their output states. Performance of the developed fatigue-detection system is assessed experimentally with a small data samples of driver trips. The obtained results are found in agreement with the state-of-the-art in terms of accuracy (90.4%), sensitivity (92.6%) and specificity (90.7%). These results are achieved with significant design flexibility and robustness against partial loss of input data source(s). However, due to small sample size of dataset (N = 3), a larger dataset need to be tested with the same system framework to generalise the findings of this work.

Acceptability of Robot Assistant in Management of Type 1 Diabetes in Children.

BACKGROUND: To find out whether children with type 1 diabetes accept a humanoid robot as an assistant in their diabetes management. In particular, the study aims to determine how the patients feel the robot may contribute to their care and how they respond to advice and education provided by the robot. SUBJECTS AND METHODS: A humanoid robot was used in clinic and its acceptability was tested over 3 months in 37 children (aged 6-16 years) with type 1 diabetes during their clinic visits. RESULTS: The obtained result showed that the overall patients' acceptability is 86.7%. However, the level of acceptability varies depending on the age group; patients aged 6-9 years showed the highest acceptability level of 94.8%, while the older age groups, 10-12 and 13-16 years, showed lower acceptability levels of 85.0% and 83.0%, respectively. There was no difference in the overall acceptability of the robot between the male and female patients (87.0% and 86.5%, respectively). Furthermore, features of the robot that were highly desirable include ability of the robot to give advice on high/low blood glucose (BG) levels (92.0%), how much the patients like the robot (91.4%), and ability of the robot to give advice on BG patterns (90.6%). In contrast, some other features were found least acceptable such as how likely patients want the robot as a companion (81.0%) and calculation of insulin doses with meals (82.53%). Analysis of variance across the responses of different age groups showed that P-value = 0.00003. CONCLUSION: Use of robots as a new device to support diabetes self-management in children was well accepted by patients.