顔面神経麻痺患者の閉瞼動作を支援するアイウェアロボット

この博士論文は内容の要約のみの公開（または一部非公開）になっています筑波大学 (University of Tsukuba)201

Propuesta de un dispositivo para rehabilitación de parálisis facial por estimulación eléctrica

El presente trabajo de investigación aborda los problemas que presentan las terapias actuales para la rehabilitación de la parálisis facial; los cuales son, por ejemplo, no conocer la intensidad de corriente adecuada y la excesiva necesidad de un terapista. Con la finalidad de solucionar estos inconvenientes, se propone un dispositivo de rehabilitación de parálisis facial por estimulación eléctrica, el cual se desarrolla en el presente trabajo y pretende ser eficaz. El primer paso para la realización de esta propuesta fue plantear como objetivo general su diseño, además se propusieron como objetivos específicos el diseño de algoritmos para regular la intensidad de corriente, el diseño de la máscara y de la interfaz gráfica de usuario. Los elementos que se diseñaran son el desarrollo de los algoritmos de procesamiento de la intensidad de corriente y voltaje y, además, el diseño de la máscara y la interfaz que observará el usuario; mientras que el diseño de los electrodos ni del microcontrolador no se consideran debido a que no es el propósito del proyecto. El siguiente paso fue realizar una revisión exhaustiva del estado del arte para la formulación de ideas, por lo que se buscó dispositivos de rehabilitación de la parálisis facial por estimulación eléctrica y máscaras que realicen dicha terapia. Se presentó, además, el marco teórico de los distintos tipos de terapias, así como las corrientes aplicadas en la electroterapia, por lo que se determinó usar corrientes de baja frecuencia e intensidad, ya que, de esta manera, no produciría daños en el usuario. A partir de ello, se realizó una lista de exigencias y una estructura de funciones del dispositivo de rehabilitación. Estos sirvieron para poder identificar tres conceptos de solución, de los cuales, mediante un análisis técnico-económico, se pudo obtener un concepto de solución ganador. Luego, se pudo realizar un diagrama de operaciones, una arquitectura de hardware y el diagrama de flujo del concepto de solución ganador. Finalmente, se presentaron conclusiones destacadas de esta propuesta.Trabajo de investigació

Effects of dance therapy on balance, gait and neuro-psychological performances in patients with Parkinson's disease and postural instability

Postural Instability (PI) is a core feature of Parkinson’s Disease (PD) and a major cause of falls and disabilities. Impairment of executive functions has been called as an aggravating factor on motor performances. Dance therapy has been shown effective for improving gait and has been suggested as an alternative rehabilitative method. To evaluate gait performance, spatial-temporal (S-T) gait parameters and cognitive performances in a cohort of patients with PD and PI modifications in balance after a cycle of dance therapy

Automated screening methods for mental and neuro-developmental disorders

Mental and neuro-developmental disorders such as depression, bipolar disorder, and autism spectrum disorder (ASD) are critical healthcare issues which affect a large number of people. Depression, according to the World Health Organisation, is the largest cause of disability worldwide and affects more than 300 million people. Bipolar disorder affects more than 60 million individuals worldwide. ASD, meanwhile, affects more than 1 in 100 people in the UK. Not only do these disorders adversely affect the quality of life of affected individuals, they also have a significant economic impact. While brute-force approaches are potentially useful for learning new features which could be representative of these disorders, such approaches may not be best suited for developing robust screening methods. This is due to a myriad of confounding factors, such as the age, gender, cultural background, and socio-economic status, which can affect social signals of individuals in a similar way as the symptoms of these disorders. Brute-force approaches may learn to exploit effects of these confounding factors on social signals in place of effects due to mental and neuro-developmental disorders. The main objective of this thesis is to develop, investigate, and propose computational methods to screen for mental and neuro-developmental disorders in accordance with descriptions given in the Diagnostic and Statistical Manual (DSM). The DSM manual is a guidebook published by the American Psychiatric Association which offers common language on mental disorders. Our motivation is to alleviate, to an extent, the possibility of machine learning algorithms picking up one of the confounding factors to optimise performance for the dataset – something which we do not find uncommon in research literature. To this end, we introduce three new methods for automated screening for depression from audio/visual recordings, namely: turbulence features, craniofacial movement features, and Fisher Vector based representation of speech spectra. We surmise that psychomotor changes due to depression lead to uniqueness in an individual's speech pattern which manifest as sudden and erratic changes in speech feature contours. The efficacy of these features is demonstrated as part of our solution to Audio/Visual Emotion Challenge 2017 (AVEC 2017) on Depression severity prediction. We also detail a methodology to quantify specific craniofacial movements, which we hypothesised could be indicative of psychomotor retardation, and hence depression. The efficacy of craniofacial movement features is demonstrated using datasets from the 2014 and 2017 editions of AVEC Depression severity prediction challenges. Finally, using the dataset provided as part of AVEC 2016 Depression classification challenge, we demonstrate that differences between speech of individuals with and without depression can be quantified effectively using the Fisher Vector representation of speech spectra. For our work on automated screening of bipolar disorder, we propose methods to classify individuals with bipolar disorder into states of remission, hypo-mania, and mania. Here, we surmise that like depression, individuals with different levels of mania have certain uniqueness to their social signals. Based on this understanding, we propose the use of turbulence features for audio/visual social signals (i.e. speech and facial expressions). We also propose the use of Fisher Vectors to create a unified representation of speech in terms of prosody, voice quality, and speech spectra. These methods have been proposed as part of our solution to the AVEC 2018 Bipolar disorder challenge. In addition, we find that the task of automated screening for ASD is much more complicated. Here, confounding factors can easily overwhelm socials signals which are affected by ASD. We discuss, in the light of research literature and our experimental analysis, that significant collaborative work is required between computer scientists and clinicians to discern social signals which are robust to common confounding factors

Applied and laboratory-based autonomic and neurophysiological monitoring during sustained attention tasks

Fluctuations during sustained attention can cause momentary lapses in performance which can have a significant impact on safety and wellbeing. However, it is less clear how unrelated tasks impact current task processes, and whether potential disturbances can be detected by autonomic and central nervous system measures in naturalistic settings. In a series of five experiments, I sought to investigate how prior attentional load impacts semi-naturalistic tasks of sustained attention, and whether neurophysiological and psychophysiological monitoring of continuous task processes and performance could capture attentional lapses. The first experiment explored various non-invasive electrophysiological and subjective methods during multitasking. The second experiment employed a manipulation of multitasking, task switching, to attempt to unravel the negative lasting impacts of multitasking on neural oscillatory activity, while the third experiment employed a similar paradigm in a semi-naturalistic environment of simulated driving. The fourth experiment explored the feasibility of measuring changes in autonomic processing during a naturalistic sustained monitoring task, autonomous driving, while the fifth experiment investigated the visual demands and acceptability of a biological based monitoring system. The results revealed several findings. While the first experiment demonstrated that only self-report ratings were able to successfully disentangle attentional load during multitasking; the second and third experiment revealed deficits in parieto-occipital alpha activity and continuous performance depending on the attentional load of a previous unrelated task. The fourth experiment demonstrated increased sympathetic activity and a smaller distribution of fixations during an unexpected event in autonomous driving, while the fifth experiment revealed the acceptability of a biological based monitoring system although further research is needed to unpick the effects on attention. Overall, the results of this thesis help to provide insight into how autonomic and central processes manifest during semi-naturalistic sustained attention tasks. It also provides support for a neuro- or biofeedback system to improve safety and wellbeing