A Systematic Review of Human–Computer Interaction (HCI) Research in Medical and Other Engineering Fields

Milani, Alireza Sadeghi; Cecil-Xavier, Aaron; Gupta, Akhil; Cecil, J.; Kennison, Shelia M.

doi:10.1080/10447318.2022.2116530

Cited by 14 publications

(4 citation statements)

References 109 publications

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“…A uni ed framework for investigating multidimensional characteristics in conjunction with body component methods for pose estimation is provided by another piece of work reported in [11].A maximum entropy Markov methods was employed as the recognition engine, and it was asserted that it successfully identi ed performed physical activity and correctly identi ed body parts. For activity recognition in [12],…”

Section: Related Workmentioning

confidence: 99%

Body Sensor Cloud Network Based Data Classification By Machine Learning Techniques In Cognitive Human Computer Interaction

D¹,

S.S

Shukla

et al. 2023

Preprint

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Recent developments in cognitive technical systems (CTS), which offer organic and effective operating principles, reveal a development in human-computer interaction (HCI). A CTS must rely on data from several sensors, which must then be processed and merged by fusion algorithms, to do this. To put the observations made into the proper context, additional knowledge sources must also be integrated. This research propose novel technique in cognitive human computer interaction based body sensor data analytics using machine learning technique. here the body sensor based monitoring data has been collected and transmitted by cloud networks for cognitive human computer interaction. then this data has been processed and trained using Boltzmann perceptron basis encoder neural network. Various body sensor-based monitored datasets are subjected to experimental analysis for accuracy, precision, recall, F-1 score, RMSE, normalised square error (NSE), and mean average precision. Proposed technique obtained 93% accuracy, 79% precision, 72% of recall, 64% f-1 score, 51% of RMSE, 56% NSE and 48% MAP.

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Section: Related Workmentioning

confidence: 99%

Body Sensor Cloud Network Based Data Classification By Machine Learning Techniques In Cognitive Human Computer Interaction

D¹,

S.S

Shukla

et al. 2023

Preprint

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“…One promising and nearly unexplored approach in HCI Education is the use of educational simulations that mimic elements of the situated and complex workplace environment. While the eld of HCI has published extensively on the usability of simulations in domains ranging from nursing to English language learning to manufacturing (e.g., Sadeghi Milani et al, 2022;Zhou et al, 2021), ironically, simulations do not show up as a method used by HCI researchers. For example, a literature review evaluating methods of teaching HCI included the search term for "simulation," but did not nd any articles that used simulations (de Souza Lima & Benitti, 2019).…”

Section: Introductionmentioning

confidence: 99%

Teaching User Experience Design in-Context: A Case Study on Immersive Learning with a Playable Case Study Simulation

Hughes,

England,

Bradley

et al. 2024

Preprint

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This paper explores the potential of using educational simulations, specifically Playable Case Studies (PCS), in Human-Computer Interaction (HCI) courses. We present Cyclops, a PCS developed for an introductory HCI class, as an example of how simulations can provide immersive learning opportunities for students in the field of HCI. The PCS Cyclops places learners in a fictional Virtual Reality game company where they take on the role of a user experience (UX) researcher. Through this simulation, learners experience HCI roles in an authentic workplace context, develop skills in analyzing user testing data, and practice ethical decision making in a low-risk environment. We report on a case study where we deployed and evaluated the use of the Cyclops PCS in an introductory HCI design class. Students reported an improved understanding of the UX profession after completing the PCS, including both the social and technical skills required for the work. They also reported an increased confidence in pursuing careers in HCI/UX. While the realism embedded in the PCS played a key role in fostering a genuine sense of the professional UX context, the paper acknowledges challenges in balancing workplace ambiguity and complexity with the need to provide students with clear structure to complete the simulation. Strategies for improving the PCS are discussed. We demonstrate how PCSs offer a versatile and adaptable educational framework that can be tailored to suit the unique challenges and objectives of HCI/UX as well as different fields.

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“…Artificial Intelligence is getting involved in our daily life with humans more and more, the line between humans and AI is getting bluer day-by-day. Today, AI can do everything that humans can from manufacturing to playing sports, from problem solving to writing code, one of the greatest example of this Human-Computer Interaction(HCI) [1]. It is a system which is able to understand the texts posed by humans in Natural Language, and it is able to answer them in the most accurate manner in the Natural Language itself [2].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Performance of Conversational Bot Using Seq2Seq Model and Attention Mechanism

Saluja,

Agarwal,

Kumar

et al. 2024

ICST Transactions on Scalable Information Systems

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The Chat-Bot utilizes Sequence-to-Sequence Model with the Attention Mechanism, in order to interpret and address user inputs effectively. The whole model consists of Data gathering, Data preprocessing, Seq2seq Model, Training and Tuning. Data preprocessing involves cleaning of any irrelevant data, before converting them into the numerical format. The Seq2Seq Model is comprised of two components: an Encoder and a Decoder. Both Encoder and Decoder along with the Attention Mechanism allow dialogue management, which empowers the Model to answer the user in the most accurate and relevant manner. The output generated by the Bot is in the Natural Language only. Once the building of the Seq2Seq Model is completed, training of the model takes place in which the model is fed with the preprocessed data, during training it tries to minimize the loss function between the predicted output and the ground truth output. Performance is computed using metrics such as perplexity, BLEU score, and ROUGE score on a held-out validation set. In order to meet non-functional requirements, our system needs to maintain a response time of under one second with an accuracy target exceeding 90%.

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A Systematic Review of Human–Computer Interaction (HCI) Research in Medical and Other Engineering Fields

Cited by 14 publications

References 109 publications

Body Sensor Cloud Network Based Data Classification By Machine Learning Techniques In Cognitive Human Computer Interaction

Body Sensor Cloud Network Based Data Classification By Machine Learning Techniques In Cognitive Human Computer Interaction

Teaching User Experience Design in-Context: A Case Study on Immersive Learning with a Playable Case Study Simulation

Evaluating Performance of Conversational Bot Using Seq2Seq Model and Attention Mechanism

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