{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T04:54:09Z","timestamp":1781844849693,"version":"3.54.5"},"reference-count":156,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:00:00Z","timestamp":1781222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Human activity recognition\u2014HAR\u2014plays a crucial role in the lives of patients battling neurodegenerative diseases. These debilitating conditions, such as Alzheimer\u2019s or Parkinson\u2019s, affect individuals\u2019 ability to perform daily tasks autonomously and safely. HAR technology offers an invaluable solution by enabling real-time monitoring and assistance, helping to maintain independence and quality of life for patients. Additionally, this technology provides a valuable data source for doctors and caregivers, allowing for more precise and personalized care, which can make a difference in managing and treating these neurodegenerative diseases. The objective of this review is to identify the contribution of Transfer Learning and Reinforcement Learning in supporting the processes of daily activity recognition, thus enhancing the quality of life for patients. As this is a trending topic, the literature surrounding it is quite dispersed, which is why this review aims to present the current line of research in this field. To carry out this analysis, the science tree paradigm was used, which establishes two fundamental stages of analysis. The first is delimited by scientometrics, where the leading countries in the application of such technologies can be identified. This review highlights the evolution in the use of transfer learning and reinforcement learning in HAR in the healthcare field, where these techniques have significantly improved the accuracy and adaptability of real-time monitoring systems. The studies reviewed indicate that transfer learning has allowed models to adapt to data variations without requiring large volumes of manual labeling, which is essential in clinical and patient monitoring contexts. Additionally, reinforcement learning has optimized decision-making in complex scenarios, enabling activity recognition systems to dynamically adjust monitoring parameters, enhancing detection and response to critical or unusual activities in multi-user environments. These advances demonstrate that, by integrating these approaches, greater personalization and robustness can be achieved in human activity recognition, thereby improving the quality of life for patients in clinical settings.<\/jats:p>","DOI":"10.3390\/s26123751","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T05:59:47Z","timestamp":1781503187000},"page":"3751","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Transfer and Reinforcement Learning as Support Paradigms for Human Activity Recognition in Indoor Environments: A Comprehensive Analysis of Trends, Impact and Future Directions"],"prefix":"10.3390","volume":"26","author":[{"given":"Paola Patricia","family":"Ariza-Colpas","sequence":"first","affiliation":[{"name":"Department of Computer Science and Electronics, Universidad de la Costa CUC, Barranquilla 080002, Colombia"},{"name":"Faculty of Engineering in Information and Communication Technologies, Universidad Pontificia Bolivariana, Medell\u00edn 050031, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marlon-Alberto","family":"Pi\u00f1eres-Melo","sequence":"additional","affiliation":[{"name":"Department of Systems Engineering, Universidad del Norte, Barranquilla 081001, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7105-7819","authenticated-orcid":false,"given":"Ana Isabel","family":"Oviedo-Carrascal","sequence":"additional","affiliation":[{"name":"Faculty of Engineering in Information and Communication Technologies, Universidad Pontificia Bolivariana, Medell\u00edn 050031, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1791-4258","authenticated-orcid":false,"given":"David","family":"D\u00edaz Jim\u00e9nez","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Ja\u00e9n, Campus Las Lagunillas, 23071 Ja\u00e9n, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6429","DOI":"10.1109\/JIOT.2020.2985082","article-title":"Deep-learning-enhanced human activity recognition for Internet of healthcare things","volume":"7","author":"Zhou","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e1254","DOI":"10.1002\/widm.1254","article-title":"Recent trends in machine learning for human activity recognition\u2014A survey","volume":"8","author":"Ramamurthy","year":"2018","journal-title":"Wiley Interdiscip. 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