Abstract

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Background: Hospital cabins are a part and parcel of the healthcare system. Most patients admitted in hospital cab- ins reside in bedridden and immobile conditions. Though diferent kinds of systems exist to aid such patients, most of them focus on specifc tasks like calling for emergencies, monitoring patient health, etc. while the patients’ limitations are ignored. Though some patient interaction systems have been developed, only singular options like touch, hand gesture or voice based interaction were provided which may not be usable for bedridden and immobile patients. Methods: At frst, we reviewed the existing literature to explore the prevailing healthcare and interaction systems developed for bedridden and immobile patients. Then, a requirements elicitation study was conducted through semi- structured interviews. Afterwards, design goals were established to address the requirements. Based on these goals and by using computer vision and deep learning technologies, a hospital cabin control system having multimodal interactions facility was designed and developed for hospital admitted, bedridden and immobile patients. Finally, the system was evaluated through an experiment replicated with 12 hospital admitted patients to measure its efective- ness, usability and efciency. Results: As outcomes, frstly, a set of user-requirements were identifed for hospital admitted patients and healthcare practitioners. Secondly, a hospital cabin control system was designed and developed that supports multimodal inter- actions for bedridden and immobile hospital admitted patients which includes (a) Hand gesture based interaction for moving a cursor with hand and showing hand gesture for clicking, (b) Nose teeth based interaction where nose is used for moving a cursor and teeth is used for clicking and (c) Voice based interaction for executing tasks using specifc voice commands. Finally, the evaluation results showed that the system is efcient, efective and usable to the focused users with 100% success rate, reasonable number of attempts and task completion time. Conclusion: In the resultant system, Deep Learning has been incorporated to facilitate multimodal interaction for enhancing accessibility. Thus, the developed system along with its evaluation results and the identifed requirements provides a promising solution for the prevailing crisis in the healthcare sector.