13 research outputs found
Synthesizing Skeletal Motion and Physiological Signals as a Function of a Virtual Human's Actions and Emotions
Round-the-clock monitoring of human behavior and emotions is required in many
healthcare applications which is very expensive but can be automated using
machine learning (ML) and sensor technologies. Unfortunately, the lack of
infrastructure for collection and sharing of such data is a bottleneck for ML
research applied to healthcare. Our goal is to circumvent this bottleneck by
simulating a human body in virtual environment. This will allow generation of
potentially infinite amounts of shareable data from an individual as a function
of his actions, interactions and emotions in a care facility or at home, with
no risk of confidentiality breach or privacy invasion. In this paper, we
develop for the first time a system consisting of computational models for
synchronously synthesizing skeletal motion, electrocardiogram, blood pressure,
respiration, and skin conductance signals as a function of an open-ended set of
actions and emotions. Our experimental evaluations, involving user studies,
benchmark datasets and comparison to findings in the literature, show that our
models can generate skeletal motion and physiological signals with high
fidelity. The proposed framework is modular and allows the flexibility to
experiment with different models. In addition to facilitating ML research for
round-the-clock monitoring at a reduced cost, the proposed framework will allow
reusability of code and data, and may be used as a training tool for ML
practitioners and healthcare professionals
Pharmacokinetic and Pharmacodynamic Considerations in Psychotropic Medication Use in the Elderly: An Overview
Synthesizing skeletal motion and physiological signals as a function of a virtual human’s actions and emotions
Round-the-clock monitoring of human behavior and emotions is required in many healthcare applications which is very expensive but can be automated using machine learning (ML) and sensor technologies. Unfortunately, the lack of infrastructure for collection and sharing of such data is a bottleneck for ML research applied to healthcare. Our goal is to circumvent this bottleneck by simulating a human body in virtual environment. This will allow generation of potentially infinite amounts of shareable data from an individual as a function of his actions, interactions and emotions in a care facility or at home, with no risk of confidentiality breach or privacy invasion. In this paper, we develop for the first time a system consisting of computational models for synchronously synthesizing skeletal motion, electrocardiogram, blood pressure, respiration, and skin conductance signals as a function of an open-ended set of actions and emotions. Our experimental evaluations, involving user studies, benchmark datasets and comparison to findings in the literature, show that our models can generate skeletal motion and physiological signals with high fidelity. The proposed framework is modular and allows the flexibility to experiment with different models. In addition to facilitating ML research for round-the-clock monitoring at a reduced cost, the proposed framework will allow reusability of code and data, and may be used as a training tool for ML practitioners and healthcare professionals
Applications of artificial intelligence to neurological disorders: Current technologies and open problems
Neurological disorders are caused by structural, biochemical, and electrical abnormalities involving the central and peripheral nervous system. These disorders may be congenital, developmental, or acute onset in nature. Some of the conditions respond to surgical interventions while most require pharmacological intervention and management, and are also likely to be progressive in nature. Owing to a high global burden of the most common neurological disorders, such as dementia, stroke, epilepsy, Parkinson’s disease, multiple sclerosis, migraine, and tension-type headache, there exist multiple challenges in early diagnosis, management, and prevention domains, which are further amplified in regions with inadequate medical services. In such situations, technology ought to play an inevitable role. In this chapter, we review artificial intelligence (AI) and machine learning (ML) technologies for mitigating the challenges posed by neurological disorders. To that end, we follow three steps. First, we present the taxonomy of neurological disorders, derived from well-established findings in the medical literature. Second, we identify challenges posed by each of the common disorders in the taxonomy that can be defined as computational problems. Finally, we review AI/ML algorithms that have either stood the test of time or shown the promise to solve each of these problems. We also discuss open problems that are yet to have an effective solution for the challenges posed by neurological disorders. This chapter covers a wide range of disorders and AI/ML techniques with the goal to expose researchers and practitioners in neurological disorders and AI/ML to each other’s field, leading to fruitful collaborations and effective solutions