Optimization of Multi-Parameter Wearable Biosensors for Continuous Chemotherapy Monitoring: Signal Reliability, Patient Adherence, and Methodological Lineage

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@article{1713179,
      author = {Caleb Joel Nwaogwugwu, Oluwayemisi Kudirat Olutunde, Amara Joan Onwuegbuchulam, Okiro Chika Jessica, Ezebuogor Ugochi Osiegbu},
      title = {Optimization of Multi-Parameter Wearable Biosensors for Continuous Chemotherapy Monitoring: Signal Reliability, Patient Adherence, and Methodological Lineage},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {6},
      pages = {600-621},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1713179.pdf},
      abstract = {Wearable biosensor technologies facilitate continuous physiological monitoring, thereby enhancing chemotherapy surveillance beyond sporadic clinic visits. This advancement provides opportunities for earlier toxicity detection and more tailored oncology care. However, clinical adoption is still limited by poor signal quality in real-world situations, problems with combining data from different types of sensors, and patients not sticking with the treatment over time.This study synthesizes empirical evidence published from 2019 to 2025 to assess optimization strategies that tackle these barriers in three essential areas: signal reliability, multi-parameter sensor fusion, and ongoing patient engagement. Quantitative analysis shows that advanced signal processing pipelines that use adaptive filtering and machine-learning-based artifact correction can improve effective signal-to-noise ratios by 20?35% compared to traditional methods. Combining three or more complementary physiological signals into a multimodal sensor fusion system increases the sensitivity of adverse-event detection from about 65% in single-sensor systems to almost 90%, while lowering the false positive rate by 30?50%. Wearable monitoring platforms that include bidirectional clinician-linked feedback also consistently achieve patient adherence rates of over 75% at three months, which is much better than passive monitoring methods. These results show a clear methodological connection between conceptual frameworks and empirically validated optimization strategies for monitoring chemotherapy with wearables. The results offer practical technical and implementation advice for creating durable, scalable, and patient-centered wearable monitoring systems that can help make oncology care safer, more proactive, and more decentralized.},
      keywords = {Wearable Biosensors; Chemotherapy Monitoring; Remote Patient Monitoring; Signal Optimization; Multi-Sensor Fusion; Digital Oncology; Patient Adherence},
      month = {December},
      doi = {https://doi.org/10.64388/IREV7I6-1713179}
  }