In this paper, we propose AFSense-ECG, an intelligence-embedded single lead ECG sensor that is enabled with the ability of accurate detection of Atrial Fibrillation (AF) condition, which is the most common sustained cardiac arrhythmia and increased risk of stroke is higher with sub-clinical AF patients. AFSense-ECG acts like an early-warning sensor for AF condition detection. A processing unit (e.g., ESP32WROVERE microcontroller) integrated with off-the-shelf single lead ECG sensor like Alivecor or AD8232 embeds intelligence to the sensing system to augment for inferential sensing for empowering automated decision-making. AFSense-ECG captures the quasi-periodic nature of typical ECG signals with repetitive P-wave, QRS complex and T-wave patterns into its feature extraction and the representation learning process of model construction and learning rate optimization. Our empirical study validates the superiority of proposed ECG signal characteristics-based hyper-parameter tuned ECG classification model construction. AFSense-ECG demonstrates F1-measure of 86.13%, where the current state-of-the-art methods report F1-measures of 83.70%, 83.10%, 82.90%, 82.60%, 82.50%, 81.00% over publicly available single lead ECG datasets of Physionet 2017 Challenge. Further, the proposed learning model for the inferential sensing is lean (approximately 25 times simpler in terms of total number of trainable parameters with reduced model size than relevant state-of-the-art model, where the state-of-the-art method with 83.70% F1-measure consists of 10474607 trainable parameters, and our proposed model consists of 433675 trainable parameters) and more effective (better F1-measure than the state-of-the-art methods), which enables us to construct affordable intelligent sensing system.

Remote and automated healthcare management has shown the prospective to significantly impact the future of human prognosis rate. Internet of Things (IoT) enables the development and implementation ecosystem to cater the need of large number of relevant stakeholders. In this paper, we consider the cardiac health management system to demonstrate that data-driven techniques produce substantial performance merits in terms of clinical efficacy by employing robust machine learning methods with relevant and selected signal processing features. We consider phonocardiogram (PCG) or heart sound as the exemplary physiological signal. PCG carries substantial cardiac health signature to establish our claim of data-centric superior clinical utility. Our method demonstrates close to 85% accuracy on publicly available MIT-Physionet PCG datasets and outperform relevant state-of-the-art algorithm. Due to its simpler computational architecture of shallow classifier with just three features, the proposed analytics method is performed at edge gateway. However, it is to be noted that healthcare analytics deal with number of sensitive data and subsequent inferences, which need privacy protection. Additionally, the problem of healthcare data privacy prevention is addressed by de-risking of sensitive data management using differential privacy, such that controlled privacy protection on sensitive healthcare data can be enabled. When a user sets for privacy protection, appropriate privacy preservation is guaranteed for defense against privacy-breaching knowledge mining attacks. In this era of IoT and machine intelligence, this work is of practical importance, which enables on-demand automated screening of cardiac health under minimizing the privacy breaching risk.

In this paper we present a lightweight security scheme for authentication and key management to establish a secure channel for Intelligent Transportation System (ITS) for an IoT (Internet of Things) application. We choose Constrained Application Protocol (CoAP) as lightweight application layer protocol. Low overhead security is still an open challenge for CoAP. We propose a payload embedded low cost symmetric-key based robust authentication and key management mechanism on CoAP. This minimizes the security overhead by eliminating expensive handshaking and ciphersuite agreement of standard TLS and DTLS. We propose some unique modification in the CoAP header to invoke its secure mode in an optimized manner. Further, we propose a secure channel with adaptive reliability which reduces the overall communication cost. Such a low overhead security scheme for CoAP is hitherto unexplored. The efficacy of our proposed scheme is demonstrated through laboratory experiments in an emulated environment.

We propose here derivation algorithms for physiological parameters like beat start point, systolic peak, pulse duration, peak-to-peak distance related to heart rate, dicrotic minima, diastolic peak from Photoplethysmogram (PPG) signals robustly. Our methods are based on unsupervised learning mainly following morphology as well as discrete nature of the signal. Statistical learning has been used as a special aid to infer most probable feature values mainly to cope up with presence of noise, which is assumed to be insignificant compared to signal values at each investigation window. Performance of the proposed method is found to be better than other standard methods, yielding precision and sensitivity more than 97% obtained from three real life data sets.

Time series sensor data classification tasks often suffer from training data scarcity issue due to the expenses associated with the expert-intervened annotation efforts. For example, Electrocardiogram (ECG) data classification for cardio-vascular disease (CVD) detection requires expensive labeling procedures with the help of cardiologists. Current state-of-the-art algorithms like deep learning models have shown outstanding performance under the general requirement of availability of large set of training examples. In this paper, we propose Shapley Attributed Ablation with Augmented Learning: ShapAAL, which demonstrates that deep learning algorithm with suitably selected subset of the seen examples or ablating the unimportant ones from the given limited training dataset can ensure consistently better classification performance under augmented training. In ShapAAL, additive perturbed training augments the input space to compensate the scarcity in training examples using Residual Network (ResNet) architecture through perturbation-induced inputs, while Shapley attribution seeks the subset from the augmented training space for better learnability with the goal of better general predictive performance, thanks to the “efficiency” and “null player” axioms of transferable utility games upon which Shapley value game is formulated. In ShapAAL, the subset of training examples that contribute positively to a supervised learning setup is derived from the notion of coalition games using Shapley values associated with each of the given inputs’ contribution into the model prediction. ShapAAL is a novel push-pull deep architecture where the subset selection through Shapley value attribution pushes the model to lower dimension while augmented training augments the learning capability of the model over unseen data. We perform ablation study to provide the empirical evidence of our claim and we show that proposed ShapAAL method consistently outperforms the current baselines and state-of-the-art algorithms for time series sensor data classification tasks from publicly available UCR time series archive that includes different practical important problems like detection of CVDs from ECG data.