Smart Sensors and Computing: Intelligent Systems for Healthcare and IoT Applications

The rapid evolution of smart sensors, intelligent computing, embedded systems with Internet of Things ((IoT) and wireless communication technologies, and continues to reshape the way we monitor, analyze, and interact with the physical world.^[1-3] Also, now days, the convergence of these technologies with artificial intelligence, machine learning is enabling innovative solutions across healthcare, industrial automation, smart infrastructure, and environmental monitoring.^[4,5] As these technologies advances, interdisciplinary research remains essential for translating scientific advances into practical, scalable, and impactful applications.

The Journal of Smart Sensors and Computing (Volume 2, Issue 2) consist of four research articles that are from rapidly growing field, with research spanning digital healthcare, biomedical data analytics, wearable health technologies, and large-scale wireless IoT networking.

In the present issue, Mapari et al. presents an affordable smart digital stethoscope designed to acquire heart sounds through real-time capture and processing into data that can estimate the heart rate (BPM) and classify heart sounds using machine learning algorithms. The system uses a modified KY-037 sound sensor and an external electret microphone to acquire heart sounds in real time, produce waveforms of heart activity, allow the estimation of heart beats per minute (BPM) via a machine learning classification system based on the features of the sound signal, and are visualized using the Streamlit web application.^[6]

Yadav et al. proposed a hybrid LASSO–WOA (hLWOA) feature selection model for thyroid disease classification that effectively reduces feature dimensionality while maintaining high predictive performance. By reducing the feature set from 25 to just 4 informative features, the proposed approach achieved an accuracy of 99.29%, improving model interpretability, reducing computational complexity, and enhancing classification efficiency for intelligent clinical decision support.^[7]

Matri et al. designed HerEase, a smart wearable therapy belt that supports menstrual pain management and personal hygiene. The device integrates heat therapy, vibration therapy, moisture monitoring, and wireless connectivity into a comfortable, user-friendly design, demonstrating the potential of wearable technologies to enhance women’s healthcare. The wearable device consists of a heating pad to relieve menstrual cramps through controlled heat therapy, a vibration motor to enhance comfort, and a moisture sensor that monitors pad saturation and provides timely user alerts. The study demonstrates that a simple sensor-based control system can deliver reliable, energy-efficient, and user-centered healthcare support.^[8]

Sayyed et al. presents a complete end-to-end hardware implementation and experimental validation of a Wi-SUN FAN network using a Raspberry Pi Compute Module 4 (CM4) Border Router and a custom EFR32FG28-based leaf node operating in the Indian Sub-GHz frequency band. To evaluate the practical applicability of Wi-SUN FAN, the authors implemented and analyzed three real-world IoT use cases: crop health monitoring through compressed image transmission, smart streetlight monitoring using energy consumption and status reporting, and industrial equipment monitoring through vibration, temperature, and acoustic sensing. The study demonstrates the versatility and scalability of Wi-SUN FAN for supporting reliable, low-power communication across diverse outdoor IoT applications.^[9]

There is no conflict of interest.

The authors declare that artificial intelligence (AI)-assisted tools were used only for language refinement, grammar improvement, and manuscript structuring purposes during the preparation of this work. All technical content, experimental implementation, results, and interpretations were independently developed and verified by the authors.

1. [1]   M. N. Bhuiyan, M. M. Rahman, M. M. Billah, D. Saha, Internet of Things (IoT): A review of its enabling technologies in healthcare applications, standards protocols, security, and market opportunities, IEEE Internet of Things Journal, 2021, 8, 10474-10498, doi: 10.1109/JIOT.2021.3062630.
2. [2]   Z. Obermeyer, E. J. Emanuel, Predicting the future-big data, machine learning, and clinical medicine, The New England Journal of Medicine, 2016, 375, 1216–1219, doi: 10.1056/NEJMp1606181.
3. [3]   P. Foltýnek, M. Babiuch, P. Šuránek, Measurement and data processing from Internet of Things modules by dual-core application using ESP32 board, Measurement and Control, 2019, 52, 970–984. doi: 10.1177/0020294019857748.
4. [4]   J. Bajwa, U. Munir, A. Nori, B. Williams, Artificial intelligence in healthcare: transforming the practice of medicine, Future Healthcare Journal, 2021, 8, e188-e194, doi: 10.7861/fhj.2021-0095.
5. [5]   D. B. Olawade, O. Z. Wada, A. O. Ige, B. I. Egbewole, A. Olojo, B. I. Oladapo, Artificial intelligence in environmental monitoring: Advancements, challenges, and future directions, Hygiene and Environmental Health Advances, 2024, 12, 100114, doi: 10.1016/j.heha.2024.100114.
6. [6]   N. M. Mapari, A. J. Managori, H. A. Naik, M. G. Chaurasiya, T. I. Kandhal, An intelligent low-cost digital stethoscope using Arduino, signal processing, and machine learning for real-time heart sound analysis, Journal of Smart Sensors and Computing, 2026, 2, 26206, doi: 10.64189/ssc.26206.
7. [7]   A. Yadav, A. Vaishnav, M. Joshi, An efficient hybrid LASSO-WOA (hLWOA) feature selection model for high-accuracy thyroid disease classification, Journal of Smart Sensors and Computing, 2026, 2, 26207, doi: 10.64189/ssc.26207.
8. [8]   D. S. Mantri, S. Salve, D. Marathe, P. Basare, S. Tekawade, V. Bhosale, HerEase: smart period pain relief and hygiene belt, Journal of Smart Sensors and Computing, 2026, 2, 26208, doi: 10.64189/ssc.26208.
9. [9]   M. A. Sayyed, S. Jafri, S. S. Shaikh, F. S. Sayed, Implementation and performance evaluation of a WI-SUN FAN for large-scale outdoor IoT applications, Journal of Smart Sensors and Computing, 2026, 2, 26209, doi: 10.64189/ssc.26209.