Towards Developing an Intelligent Wheelchair for People with Congenital Disabilities and Mobility Impairment

Hossain, Tasneea; Sabbir, Sabbir-Ul-Alam; Mariam, Asma; Inan, Toki Tahmid; Islam, Muhammad Nazrul; Mahbub, Khairul; Sazid, Muhammad Tawsif

doi:10.1109/icasert.2019.8934522

Cited by 4 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The underlying foundation for the algorithms that are employed in this system is deep learning, and hence the effectiveness and precision of the functioning of the system in real time have been improved. There have been several different digital systems created to provide medical treatment to those who are unable to care for themselves due to illness or disability and those in advanced years (Lakkis and Elshakankiri, 2017;Khan et al, 2018;Hossain et al, 2019;Hasan et al, 2021;Islam et al, 2021). In Kanase and Gaikwad (2016), the authors suggested using a cloudbased approach that uses a variety of sensory data.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of a Smart Hospital Bed Based on Deep Learning to Monitor Patient Conditions

Ayouni¹,

Maddeh²,

Al-Otaibi³

et al. 2023

Journal of Disability Research

View full text Add to dashboard Cite

An Internet of Things-based automated patient condition monitoring and detection system is discussed and built in this work. The proposed algorithm that underpins the smart-bed system is based on deep learning. The movement and posture of the patient’s body may be determined with the help of wearable sensor-based devices. In this work, an internet protocol camera device is used for monitoring the smart bed, and sensor data from five key points of the smart bed are core components of our approach. The Mask Region Convolutional Neural Network approach is used to extract data from many important areas from the body of the patient by collecting data from sensors. The distance and the time threshold are used to identify motions as being either connected with normal circumstances or uncomfortable ones. The information from these key locations is also utilised to establish the postures in which the patient is lying in while they are being treated on the bed. The patient’s body motion and bodily expression are constantly monitored for any discomfort if present. The results of the experiments demonstrate that the suggested system is valuable since it achieves a true-positive rate of 95% while only yielding a false-positive rate of 4%.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Development of a Smart Hospital Bed Based on Deep Learning to Monitor Patient Conditions

Ayouni¹,

Maddeh²,

Al-Otaibi³

et al. 2023

Journal of Disability Research

View full text Add to dashboard Cite

show abstract

Developing a Smart System for Reducing Traffic Congestion

Promy¹,

Rawshan²,

Islam³

2022

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

A deep learning based multimodal interaction system for bed ridden and immobile hospital admitted patients: design, development and evaluation

Islam¹,

Aadeeb²,

Munna³

et al. 2022

BMC Health Serv Res

View full text Add to dashboard Cite

Background Hospital cabins are a part and parcel of the healthcare system. Most patients admitted in hospital cabins reside in bedridden and immobile conditions. Though different kinds of systems exist to aid such patients, most of them focus on specific 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 first, 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 effectiveness, usability and efficiency. Results As outcomes, firstly, a set of user-requirements were identified for hospital admitted patients and healthcare practitioners. Secondly, a hospital cabin control system was designed and developed that supports multimodal interactions 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 specific voice commands. Finally, the evaluation results showed that the system is efficient, effective 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 identified requirements provides a promising solution for the prevailing crisis in the healthcare sector. Trial Registration Not Applicable.

show abstract

Towards Developing an Intelligent Wheelchair for People with Congenital Disabilities and Mobility Impairment

Cited by 4 publications

References 11 publications

Development of a Smart Hospital Bed Based on Deep Learning to Monitor Patient Conditions

Development of a Smart Hospital Bed Based on Deep Learning to Monitor Patient Conditions

Developing a Smart System for Reducing Traffic Congestion

A deep learning based multimodal interaction system for bed ridden and immobile hospital admitted patients: design, development and evaluation

Contact Info

Product

Resources

About