Can the Latest Computerized Technologies Revolutionize Conventional Assessment Tools and Therapies for a Neurological Disease? The Example of Parkinson's Disease.

Dramatic breakthroughs in the treatment and assessment of neurological diseases are lacking. We believe that conventional methods have several limitations. Computerized technologies, including virtual reality, augmented reality, and robot assistant systems, are advancing at a rapid pace. In this study, we used Parkinson's disease (PD) as an example to elucidate how the latest computerized technologies can improve the diagnosis and treatment of neurological diseases. Dopaminergic medication and deep brain stimulation remain the most effective interventions for treating PD. Subjective scales, such as the Unified Parkinson's Disease Rating Scale and the Hoehn and Yahr stage, are still the most widely used assessments. Wearable sensors, virtual reality, augmented reality, and robot assistant systems are increasingly being used for evaluation of patients with PD. The use of such computerized technologies can result in safe, objective, real-time behavioral assessments. Our experiences and understanding of PD have led us to believe that such technologies can provide real-time assessment, which will revolutionize the traditional assessment and treatment of PD. New technologies are desired that can revolutionize PD treatment and facilitate real-time adjustment of treatment based on motor fluctuations, such as telediagnosis systems and "smart treatment systems." The use of these technologies will substantially improve both the assessment and the treatment of neurological diseases before next-generation treatments, such as stem cell and genetic therapy, and next-generation assessments, can be clinically practiced, although the current level of artificial intelligence cannot replace the role of clinicians.

Discriminating between mothers' infant- and adult-directed speech: Cross-linguistic generalizability from Japanese to Italian and German.

The aim of the present work was a cross-linguistic generalization of Inoue et al.'s (2011) algorithm for discriminating infant- (IDS) vs. adult-directed speech (ADS). IDS is the way in which mothers communicate with infants; it is a universal communicative property, with some cross-linguistic differences. Inoue et al. (2011) implemented a machine algorithm that, by using a mel-frequency cepstral coefficient and a hidden Markov model, discriminated IDS from ADS in Japanese. We applied the original algorithm to two other languages that are very different from Japanese - Italian and German - and then tested the algorithm on Italian and German databases of IDS and ADS. Our results showed that: First, in accord with the extant literature, IDS is realized in a similar way across languages; second, the algorithm performed well in both languages and close to that reported for Japanese. The implications for the algorithm are discussed.