Lightweight Blockchain Enabled Privacy-Preserving IoT Framework for Secure Parkinson’s Disease Fall Detection and Healthcare Monitoring

Abstract

The increased number of PD patients has increased the adoption of the Internet of Things (IoT)-based wearable devices for remote monitoring and automatic fall detection, which helps to notify caregivers and clinicians immediately. Unfortunately, the continuous transmission of patients' physiological and movement data through wireless sensor networks increases their vulnerability to risks of unauthorized access, poor authentication, and privacy loss. Most importantly, current automatic fall detection systems offer low levels of data verification and protection. To overcome this issue, the proposed study introduces the Cross-Layer Lightweight Starvation Blockchain Security scheme with the Provable Master Seeding Authentication Policy (LWSBS-PMSAP) for preserving privacy in the PD monitoring system. The data collected from the IoT wearable devices is first encrypted with the Advanced Shuffle Standard Starvation Encryption Cryptography (ASS-SEC) and packaged in the form of blockchain blocks, which are then stored on the encrypted servers; a controller node performs the verification and validation of the policy to prevent any unauthorized interference, while a zero-knowledge proof-based Provable Master Seeding Authentication (PMSA) protocol verifies devices and users. The framework was tested on the PD monitoring dataset of 100 patients having 1,500 sensor samples. LWSBS-PMSAP obtained a 98.7% throughput rate, 180 ms latency, 99.2% security rate, and 96.8% privacy preservation, and performed better than Ethereum-based, Hyperledger-based, and general lightweight blockchain frameworks. In the context of fall detection, the proposed framework attained 98.7% accuracy, 97.4% precision, 99.0% recall, and 98.2% F1-Score, performing better than CNN-based, LSTM-based, and ensemble-DNN-based models. The ablation study also proved that the contribution of each component in terms of security and privacy gain is measurable. Further, the statistical validation (mean, standard deviation, 95% confidence interval) revealed that there is low variation in performance of the proposed framework among the multiple trials. It can be concluded that LWSBS-PMSAP provides a computationally lightweight, secure, and privacy-preserving solution for real-time fall detection and remote monitoring of Parkinson's Disease, with clear potential for extension to broader IoT-based healthcare applications.