OBJECTIVES: Insufficient preparation for children who are undergoing bone marrow aspiration can cause anxiety and negative outcomes. Nonpharmacological therapies have been proven to reduce fear in children who are undergoing painful procedures. We have therefore developed a mobile application to help reduce these patients’ anxiety by providing them with procedural information and coping skills. METHODS: This single-blinded, randomized controlled trial included 60 patients age 5 to 12 years old who were undergoing bone marrow aspiration procedures in Thailand that were conducted between May 2015 and May 2016. Sixty participants were randomly assigned to the intervention group (mobile application added to usual care) or the control group (usual care only). Preprocedural anxiety levels were evaluated by visual analog scales (child anxiety visual analog scale); this was repeated in the intervention group immediately after patients used the mobile application. On the day of the procedure, the patients’ cooperation levels were assessed by using the modified Yale Preoperative Anxiety Scale. The total amount of sedative drugs that were used was also recorded. The paired t test and the Wilcoxon signed rank test were used to analyze within-person change, whereas the t test and the Wilcoxon rank sum test were used for group comparisons. RESULTS: The child anxiety visual analog scale score of patients in the intervention group decreased significantly after they used the mobile application (P < .0012). The modified Yale Preoperative Anxiety Scale score of patients in the intervention group was significantly lower than that in the control group (P < .01). There was no difference in sedative use between the 2 groups. CONCLUSIONS: This mobile application possibly had effectiveness in routine use for reducing anxiety and increasing patients’ cooperation in bone marrow aspiration procedures.

Abstract. New architectures for Brain-Machine Interface communication and control use mixture models for expanding rehabilitation capabilities of disabled patients. Here we present and test a dynamic data-driven (BMI) Brain-Machine Interface architecture that relies on multiple pairs of forward-inverse models to predict, control, and learn the trajectories of a robotic arm in a real-time closedloop system. A method of window-RLS was used to compute the forwardinverse model pairs in real-time and a model switching mechanism based on reinforcement learning was used to test the ability to map neural activity to elementary behaviors. The architectures were tested with in vivo data and implemented using remote computing resources.

This paper presents a novel online scheduling algorithm for scheduling real-time adaptive systems in which tasks may have distinct resource requirements for each of the systems' operating modes. Apart from prior work that considers only step-wise adaptation of tasks' resource utilization during mode transition, the proposed algorithm (named EAGLE-T) enables tasks to adapt their resource utilization progressively from one mode to another in a timely manner without causing any deadline miss. The upper bound of the delay and the drift between resource utilization achieved by EAGLE-T and the ideal scheduler during mode transition are provided. Performance evaluation shows that the progressive adaptation of EAGLE-T offers improved performance over a step-wise approach (average maximal-utilization drift and mode-transition delay are reduced by up to 68.75% and 32.16%, respectively). As the probability of a mode change or the number of tasks vary, empirical results show that the resource utilization achieved by tasks scheduled using EAGLE-T is within 56% to 90% of the desired utilization (compared to 11%-81% when the step-wise scheme is used).

Dynamic data-driven brain-machine interfaces (DDDBMI) have great potential to advance the understanding of neural systems and improve the design of brain-inspired rehabilitative systems. This paper presents a novel cyberinfrastructure that couples in vivo neurophysiology experimentation with massive computational resources to provide seamless and efficient support of DDDBMI research. Closed-loop experiments can be conducted with in vivo data acquisition, reliable network transfer, parallel model computation, and real-time robot control. Behavioral experiments with live animals are supported with real-time guarantees. Offline studies can be performed with various configurations for extensive analysis and training. A Web-based portal is also provided to allow users to conveniently interact with the cyberinfrastructure, conducting both experimentation and analysis. New motor control models are developed based on this approach, which include recursive least square based (RLS) and reinforcement learning based (RLBMI) algorithms. The results from an online RLBMI experiment shows that the cyberinfrastructure can successfully support DDDBMI experiments and meet the desired real-time requirements.

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