An intelligent healthcare monitoring framework using wearable sensors and social networking data
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The H-LSTM method outperformed other DL methods, achieving 99.15% accuracy in recognizing human activities.
“The results demonstrated that H-LSTM outperformed other DL methods, achieving an impressive 99.15% accuracy in recognizing human activities.”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [193]
Bidirectional LSTM technology could categorize patients' health conditions based on data related to diabetes, blood pressure, mental health, and drug reviews.
“For instance, bidirectional long short-term memory (LSTM) technology could categorize patients’ health conditions based on their data related to diabetes, blood pressure, mental health, and drug reviews.”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [193]
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The emergence of AI-based wearable sensors for digital health technology: a review
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Deep Human Motion Detection and Multi-Features Analysis for Smart Healthcare Learning Tools
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- Authors
- Ali, F., El-Sappagh, S., Islam, S. M. R., Ali, A., Attique, M., Imran, M., & Kwak, K.-S.
- Affiliations
- Sejong University (KR)
- COMSATS University Islamabad (PK)
- King Saud University (SA)
- Inha University (KR)
- Corresponding author
- Ali, Amjad
- Kwak, Kyung-Sup
- Venue
- Future Generation Computer Systems
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 114
- Pages
- 23-43
- ISSN
- 0167-739X (ISSN-L)
- OpenAlex
- W3044806259
- Cited by
- 355
- References
- 85
- Citations by year
- 2026: 18 · 2025: 32 · 2024: 66 · 2023: 84 · 2022: 49 · 2021: 81 · 2020: 25
- FWCI
- 52.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Future Generation Computer Systems (publishedVersion)
- FedUni ResearchOnline (Federation University Australia) (submittedVersion)
- Funders
- National Research Foundation of Korea (2020R1A2B5B02002478)
- Sejong university
- Ministry of Education (2020R1G1A1013221)
- King Saud University (RG-1435-051)
- Spanish Ministry of Science, Innovation and Universities (RTI2018-099646-B-I00, TIN2017-84796-C2-1-R, TIN2017-90773-REDT, RED2018-102641-T)
- Galician Ministry of Education, University and Professional Training (ED431F 2018/02, ED431C 2018/29, ED431G/08, ED431G2019/04)
- European Regional Development Fund
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Health Sciences › Health Professions › Health Information Management › Innovation in Digital Healthcare Systems
- Keywords
- healthcare monitoring, wearable sensors, social networking data, big data analytics, cloud computing, ontology (information science), bidirectional LSTM, drug side effect prediction, diabetes mellitus, blood pressure, mental health, drug review, data heterogeneity, data mining, dimensionality reduction
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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