International practice guidelines recommend medication and behavioral intervention as evidenced-based treatments for attention deficit hyperactivity disorder (ADHD). Currently in Japan, the availability of non-pharmacological interventions for ADHD is limited. We report the results of a pilot and a proof-of-concept study for a new behavioral intervention for Japanese mothers of children with ADHD. The pilot study delivered a standard six-session behavioral intervention and two parent-support sessions. Participants approved the group format and requested additional support to change parenting practices and behavioral strategies targeting ADHD symptoms. For the proof-of-concept study, the intervention was revised to include five sessions of pre-intervention support followed by six sessions of the New Forest Parent Training Programme (NFPP), an evidence-based intervention for ADHD. The revised intervention, NFPP-Japan, was associated with reductions in the mothers' reports of children's ADHD symptoms and aggression, more effective parenting practices, and reduced parenting stress. The pilot and proof-of-concept studies indicate that it is possible to successfully modify Western behavioral interventions for Japanese mothers and to justify a randomized controlled trial evaluation of the NFPP-Japan, which is currently underway.

Featured Application: This work is intended for the development of myoelectric prosthetic hand systems. Furthermore, the outcome of this study may also benefit other electromyographic based human-machine interfaces. Abstract:The myoelectric prosthetic hand is a powerful tool developed to help people with upper limb loss restore the functions of a biological hand. Recognizing multiple hand motions from only a few electromyography (EMG) sensors is one of the requirements for the development of prosthetic hands with high level of usability. This task is highly challenging because both classification rate and misclassification rate worsen with additional hand motions. This paper presents a signal processing technique that uses spectral features and an artificial neural network to classify 17 voluntary movements from EMG signals. The main highlight will be on the use of a small set of low-cost EMG sensor for classification of a reasonably large number of hand movements. The aim of this work is to extend the capabilities to recognize and produce multiple movements beyond what is currently feasible. This work will also show and discuss about how tailoring the number of hand motions for a specific task can help develop a more reliable prosthetic hand system. Online classification experiments have been conducted on seven male and five female participants to evaluate the validity of the proposed method. The proposed algorithm achieves an overall correct classification rate of up to 83%, thus, demonstrating the potential to classify 17 movements from 6 EMG sensors. Furthermore, classifying 9 motions using this method could achieve an accuracy of up to 92%. These results show that if the prosthetic hand is intended for a specific task, limiting the number of motions can significantly increase the performance and usability.

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