Sepsis is a major cause of premature mortality, high healthcare costs, and disability-adjusted life years. Digital interventions such as continuous cardiac monitoring solutions can help to monitor the patient’s status and provide valuable feedback to clinicians to detect early warning signs and provide effective interventions. This paper presents i-CardiAx, a wearable sensor based on low-power high-sensitivity accelerometers that measures vital signs essential for cardiovascular health monitoring, namely, heart rate (HR), blood pressure (BP), and respiratory rate (RR). A dataset has been collected from 10 healthy subjects with the i-CardiAx wearable chest patch to develop low-complexity, lightweight vital sign measurement algorithms and evaluate their performance. The experimental evaluation demonstrates high-performance vital sign measurement for RR (-0.11±0.77 breaths per minute), HR ( 0.82± 2.85 beats per minute), and systolic BP (-0.08 ± 6.245 mm of Hg). The proposed algorithms are embedded on the ARM Cortex-M33 processor supporting Bluetooth Low Energy (BLE). Estimation of HR and RR achieved an inference time of only 4.2 ms and 8.5 ms for BP. Moreover, a multi-channel quantized Temporal Convolutional Neural (TCN) Network has been proposed and trained on the open-source HiRID dataset to have a large number of patients with data for sepsis ground truth. The model has been trained and evaluated using only digitally acquired vital signs as input-data that could be collected by i- CardiAx to detect the onset of Sepsis in a real-time scenario. The TCN has been fully quantized to 8-bit integers and deployed on i-CardiAx.The network showed a median predicted time to sepsis of 8.2 hours with an energy per inference of 1.29mJ. i-CardiAx has a sleep power of 0.152 mW and an averages a power of 0.77 mW for always-on sensing and periodic on-board processing and BLE transmission. With a small 100 mAh battery, the operational longevity of the wearable has been estimated at two weeks (432 hours) for measuring the three cardiovascular parameters (HR, BP and RR) at a granularity of 30 measurements per hour per vital sign, running inference every 30 minutes. Thus, the wearable i-CardiAx system can provide a method to monitor the cardiovascular parameters of patients with energy-efficient, high- sensitivity sensors to provide predictive alerts for life-threatening adverse events of sepsis, over a long period of time.
There’s a significant move towards the adoption of Post-Quantum Cryptography (PQC). While there have been initiatives to transition conventional TCP/IP-based networks to PQC, Quantum Resilient Internet of Things (IoT) networks have not been as widely discussed. Presently, Bluetooth Low Energy (BLE) employs the Elliptic Curve Diffie-Hellman algorithm for Secure Connection (SC) pairing, which is vulnerable to quantum threats. In this study, we introduce a backward-compatible Post-Quantum Key Exchange (PQKE) protocol for BLE, utilizing the Kyber-512 algorithm that has been adopted by the National Institute of Standards and Technology as a post quantum Key Encapsulation Mechanism. Although Kyber-512 is quantum resilient, it has large key pairs and ciphertexts, which presents critical challenges for the limited computational resources of IoT devices. Our performance assessment reveals that with an Attribute Protocol Maximum Transmission Unit (ATT MTU) of 65 bytes, the pairing time increases by approximately 9 folds using our PQKE in comparison to the traditional BLE SC pairing, mainly due to increased data transmission. Nevertheless, by employing a larger ATT MTU, the pairing time of our PQKE mechanism can be minimized to be of the same order of magnitude as current pre-quantum key exchange for BLE. We therefore advocate for the adoption of larger ATT MTU sizes in quantum resilient BLE pairing to ensure the performance and usability of the technology in a post-quantum world.
In recent years, there has been a notable shift towards automation in agricultural technology. Furthermore, driven by environmental concerns, green energy is gradually replacing conventional power sources. However, green energy sources are vulnerable to external environmental factors, resulting in an unreliable power supply. Such instability can disrupt the functioning of monitoring systems which are critical for agricultural automation, leading to operational issues. To address these challenges, we propose an intermittent edge computing system for agricultural automation. It consists of battery-less intermittent computing devices, wireless intermittent communications, and deep-capable edge computing. Our demo illustrates the process of tomato monitoring including reliable on- device tomato detection and Bluetooth Low Energy (BLE) image data transmission, even in the absence of stable power conditions. Upon receiving the data, we employ edge computing to detect the location, color, and quantity of tomatoes.
Emergency Medical Services (EMS) responders often operate under time-sensitive conditions, facing cognitive overload and inherent risks, requiring essential skills in critical thinking and rapid decision-making. This paper presents CognitiveEMS, an end-to-end wearable cognitive assistant system that can act as a collaborative virtual partner engaging in the real-time acquisition and analysis of multimodal data from an emergency scene and interacting with EMS responders through Augmented Reality (AR) smart glasses. CognitiveEMS processes the continuous streams of data in real-time and leverages edge computing to provide assistance in EMS protocol selection and intervention recognition. We address key technical challenges in real-time cognitive assistance by introducing three novel components: (i) a Speech Recognition model that is fine-tuned for real-world medical emergency conversations using simulated EMS audio recordings, augmented with synthetic data generated by large language models (LLMs); (ii) an EMS Protocol Prediction model that combines state-of-the-art (SOTA) tiny language models with EMS domain knowledge using graph-based attention mechanisms; (iii) an EMS Action Recognition module which leverages multimodal audio and video data and protocol predictions to infer the intervention/treatment actions taken by the responders at the incident scene. Our results show that for speech recognition we achieve superior performance compared to SOTA (WER of 0.290 vs. 0.618) on conversational data. Our protocol prediction component also significantly outperforms SOTA (top-3 accuracy of 0.800 vs. 0.200) and the action recognition achieves an accuracy of 0.727, while maintaining an end-to-end latency of 3.78s for protocol prediction on the edge and 0.31s on the server.
Due to new regulations regarding privacy and the increasing reservations of citizens about surveillance systems, cities are exploring privacy-friendly technologies for crowd monitoring. An alternative gaining significant interest is mmWave radar for people counting applications. However, most studies in mmWave monitoring primarily focus on indoor or outdoor deployments under ideal conditions. Working with the municipality of Ams-terdam, we propose a method for counting people with mmWave in a real outdoor scenario. These realistic scenarios pose two significant difficulties. First, it is hard to estimate the number of people when they walk together because the radar captures the group as a single-point cloud. Second, the dynamic environment adds random points (noise) that may get reflected as false counts. To tackle these challenges, we optimize a state-of-the-art deep learning model to attain noise filtering and cluster disaggregation. We deploy the radar across a street and measure its performance under real traffic conditions. Our system achieves an accuracy of 96% for Presence detection and 69% for People counting. Over the long term, the system can monitor the crowd level with an error below 2.5%. Furthermore, considering the needs of our application, we reduce the complexity of the original SoA model by 93%. Our results indicate that mmWave is a promising solution for privacy-friendly people counting in urban scenarios and exposes unique challenges that have not been reported or considered in the SoA.
