IoT and I2C Protocol Based M-Health Medication Assistive System for Elderly People

Kumar, Manukonda Praveen; Nelakuditi, Usha Rani

doi:10.1109/indicon47234.2019.9030322

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…Individuals who are educated and have been exposed to ICT for their occupational purposes are less anxious and are more self-reliant and confident while using web-based technology. However, most of Indian users including professionals are unaware about the developments in ICT domain, hence it is found that it as a major challenge of adopting assistive and enabling technology (Kumar et al, 2009). Nevertheless, the digital exclusion of a large segment of older users of ICT in India are linked to lack of accessibility to various technologies, limiting their adoption of technology (William & Murugesh, 2018).…”

Section: Discussionmentioning

confidence: 99%

Information and communication technology adoption among the older people: A qualitative approach

Mitra¹,

Singh²,

Deepam³

et al. 2022

Health Social Care Comm

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Information and communication technology (ICT) can empower the older people and improve their quality of life by sustaining functional independence and effective communication. Nonetheless, in developing countries like India, technology acceptance and use among the older people is limited. Therefore, there is a need to systematically understand the plausible difficulties limiting technology use and the expectations of the older people regarding ICT use. Hence, the present study is a qualitative inquiry to explore the needs, attitudes and perceived limitations of the ICT platforms among the older people. Twenty healthy volunteers across various states of India, between 60 and 75 years were recruited online via purposive sampling. A brief demographic survey was administered online using Google Forms, followed by telephonic semi‐structured interviews. Thematic Analysis using inductive and deductive coding was employed to analyse the interview transcripts. Thematic analysis yielded four overarching themes‐ (i) Usability of ICT, (ii) Anxiety and privacy concerns, (iii) Self‐perception of technology use and (iv) Need for technology literacy. The emerging themes reflected the perceptions of the older people towards ICT and identified several psycho‐social factors impacting technology acceptance among the older people. The findings suggest that several psychological factors like anxiety, motivation, interest and self‐efficacy play a role in technology acceptance among the older people. Additionally, user‐friendly interfaces and training on technology use may enhance technology acceptance among older users.

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Section: Discussionmentioning

confidence: 99%

Information and communication technology adoption among the older people: A qualitative approach

Mitra¹,

Singh²,

Deepam³

et al. 2022

Health Social Care Comm

View full text Add to dashboard Cite

show abstract

“…Daramola et al [12] followed a similar method by reporting medicine intake using smartphone application and rely on patient self-reporting which does not guarantee medicine intake. Kumar et al [13] proposed a similar approach for medicine adherence tracking using medicine dispenser. This method assumes that the medicine was taken whenever the patient opens the dispenser to take medicine.…”

Section: Literature Reviewmentioning

confidence: 99%

A Scalable Cloud-Based Medical Adherence System with Data Analytic for Enabling Home Hospitalization

Sherif

Tan

Pun

et al. 2021

Lecture Notes in Electrical Engineering

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Medication non-adherence is one of the most significant concerns in managing chronic diseases which has inevitable consequences. While various technologies and research have been developed and carried out to monitor medical adherence for patients, their approaches lack in terms of the assurance of medicine consumption and the cost effectiveness of their solutions. This paper provides a cloudbased medical adherence system that can track patients' medicine intake based on the physical effects of the medicine on their bodies by tracking their vital signs. A machine learning model is trained to classify the patient health status and this data is used to determine whether their bodies are responding to the medicine, which is used to alert doctors to enable home hospitalization. The use of this system is proposed to serve as a secondary decision support provider to compliment and ease the decision-making process done by doctors.

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“…A lot of research has been focused on medical adherence and home hospitalisation. [5][6][7][8][9][10][11][12] Patients' in-hospital days were reduced when they were given a home hospitalisation option. 5 The abilities to remotely monitor the patient's health state and check their adherence to prescriptions are critical factors for enabling home hospitalisation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Similarly, Daramola et al 11 used a smartphone application to report medicine consumption and relied on patient selfreporting. Kumar et al 12 presented a similar method for measuring medication adherence using medicine dispensers. However, these approaches do not guarantee that the medicines have been consumed.…”

Section: Literature Reviewmentioning

confidence: 99%

Health status classification model for medical adherence system in retirement township

et al. 2021

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Medical adherence and remote patient monitoring have gained huge attention from researchers recently, especially with the need to observe the patients’ health outside hospitals due to the ongoing pandemic. The main goal of this research work is to propose a health status classification model that provides a numerical indicator of the overall health condition of a patient via four major vital signs, which are body temperature, blood pressure, blood oxygen saturation level, and heart rate. A dataset has been prepared based on the data obtained from hospital records, with these four vital signs extracted for each patient. This dataset provides a label associating each patient to the number of medical diagnoses. Generally, the number of diagnoses correlates with the patient's medical condition, with no diagnoses indicating normal condition, one to two diagnoses suggest low risk, and more than that implies high risk. Thus, we propose a method to classify a patient’s health status into three classes, which are normal, low risk and high risk. This would provide guidance for healthcare workers on the patient's medical condition. By training the classification model using the prepared dataset, the seriousness of a patient's health condition can be predicted. This prediction is performed by classifying the patients based on their four vital signs. Our tests have yielded encouraging results using precision and recall as the evaluation metrics. The key outcome of this work is a trained classification model that quantifies a patient's health condition based on four vital signs. Nevertheless, the model can be further improved by considering more input features such as medical history. The results obtained from this research can assist medical personnel by providing a secondary advice regarding the health status for the patients who are located remotely from the medical facilities.

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IoT and I2C Protocol Based M-Health Medication Assistive System for Elderly People

Cited by 5 publications

References 5 publications

Information and communication technology adoption among the older people: A qualitative approach

Information and communication technology adoption among the older people: A qualitative approach

A Scalable Cloud-Based Medical Adherence System with Data Analytic for Enabling Home Hospitalization

Health status classification model for medical adherence system in retirement township

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