Development of an IoT-Based Smart Cane with Non-Invasive Health Monitoring for Elderly Care in Batam - Putera, Dimas Akmarul; Adi, Roni; Kurniawan, Dwi Ely; Leman, Abdul Mutalib; Raynold, Raynold (Journal of Applied Informatics and Computing, 2025)
The rapid growth of the elderly population requires assistive technologies that support mobility, health, and safety. This study presents the development of an IoT-based smart cane designed to enhance elderly independence and health monitoring in Batam, Indonesia. The prototype integrates non-invasive health sensors (MAX30102 for heart rate and SpO₂, MLX90614 for temperature, and a non-invasive glucose sensor), a GPS module, a mini-CCTV with two-way audio, and a solar-powered energy system, all controlled by an ESP32 microcontroller connected to the Blynk IoT platform. Ergonomic design was guided by anthropometric data of Indonesian elderly to ensure user comfort and usability. Experimental results demonstrated stable performance of the integrated modules. Heart rate values ranged from 86–103 BPM (mean 89.5 ± 6.2 BPM), blood glucose estimations from 110–112 mg/dL (mean 111 ± 0.9 mg/dL), and body temperature from 36.9–37.1 °C (mean 37.0 ± 0.1 °C), all of which aligned closely with clinical references. Oxygen saturation readings, however, averaged 89 ± 0.8%, slightly below the clinical norm (≥95%), highlighting the need for sensor calibration. Dynamic testing of the GPS module across a 500-meter route achieved positional accuracy within 3–5 meters, while the CCTV system successfully streamed live video but was dependent on WiFi stability.The novelty of this research lies in the unique combination of locally adapted ergonomic design, multi-sensor non-invasive health monitoring, two-way visual and audio communication, GPS tracking, and renewable energy integration within a single portable device. These contributions not only enrich IoT-based healthcare research but also provide practical solutions tailored to elderly care in Indonesia. Future work will focus on clinical-grade validation of sensors, extended field trials, and the integration of predictive analytics using Machine Learning and Fuzzy Logic.