According to WHO, about 1.86 million people in Nigeria and about 24 million people worldwide are living with schizophrenia, having symptoms varying from hallucination to delusion, and distorted speech and thinking. Schizophrenia is a life-long disorder with no cure and thus, patients need continuous management with medications and psychotherapy. However, due to various factors such as the cost of therapy, time consumption, lack of adequate health workers, the unwillingness of patients to engage, and the pandemic, there is a need for an effective alternate medium for providing cognitive behavioural therapy (CBT) to schizophrenia patients. This research aims to develop a chatbot, which is called SchizoBot, delivering CBT for augmented management of schizophrenia. CBT for schizophrenia details, along with FAQs of schizophrenia patients were collected and adopted into a conversational format for pre-processing and model development. The model was developed with artificial neural network (ANN) and trained with the dataset which was split into train-test data to optimize the performance of the model. The result of the ANN showed an accuracy score of 93.97% at 60:40 train-test data split with 200 epochs. This robust system which provides an optimized chatbot platform using ANN as the model classifier for CBT delivery is foreseen to be a windfall to clinicians and patients as an augmentative management tool for schizophrenia. This, therefore, is a relatively low-cost and easily accessible means to significantly improve the health of schizophrenia patients while assisting clinicians in therapy delivery and compensating for the lapses in the administration of CBT to schizophrenia patients.

Children with an autism spectrum disorder tend to lose focus quickly. The possibility of displaying changeable, three-dimensional environments in virtual reality can increase the user's concentration time due to the stimuli used. Through the use of virtual reality, an individual assistant can be provided for the daily care and encouragement of learning for children with autism spectrum disorders. To investigate the feasibility of a virtual assistant and the acceptability of virtual reality among autistic children, a review of the literature and conducted studies is undertaken. The selection, identification and evaluation of the relevant research results is carried out according to the preferred reporting items of the systemic literature search.

This research employs non-contact plantar 3D data scanning and gait analysis methodologies to establish a rehabilitation assistance system tailored for foot arch anomalies. The system utilizes a non-contact plantar 3D data model to mitigate dysfunctions within the plantar skeletal-muscular system. Its objectives include facilitating personalized remote diagnosis of foot arch anomalies, enabling patients to monitor their rehabilitation progress, and supporting at-home rehabilitation efforts. A dataset comprising 124 cases of physiological foot arch anomalies in adults aged 18 and above was collected and analyzed. The findings demonstrate the system’s flexibility, high spatial resolution, personalization, and innovation. Notably, the system achieves real-time measurement of positive pressure and shear force distribution at the plantar interface, facilitates the construction of accurate geometric models, and yields high-quality plantar three-dimensional coordinate data. This research contributes theoretical and technical underpinnings for the application of footwork anomaly diagnosis and correction.