This work evaluates the performance of a non-supervised clustering method for identifying abrupt changepoints in streaming sensor data. The proposed method utilizes the Jenks Natural Breaks (JNB) algorithm, applied in near real-time using sliding temporal windows to analyze sections of sensor data and identify instances of significant phase changes. It is suitable for sensing applications that rely on detecting instantaneous changes in the sensed data for fast decisions, such as fire alarms, fault detection, and activity recognition. The method was applied to a custom dataset from twelve electrodes transitioning between different materials. Performance was evaluated based on detection accuracy and delay comparisons. Results demonstrate that applying JNB in a sliding window with a step size of half its length achieves the highest detection accuracy and the lowest error delay compared to non-overlapping windows.

Wearable electroencephalography (EEG) devices allow non-invasive brain monitoring during everyday activities and are widely used in managing conditions like epilepsy. However, EEG signals are small, and often corrupted by artifacts during real-world recordings. Many artificial intelligence models for EEG artifact removal have been proposed recently, but their real-time deployment on edge hardware, suitable for embedding in an EEG device itself, has remained unrealized until now. This paper introduces the first implementation of a deep autoencoder network for EEG artifact removal on edge hardware, using three embedded systems: an Arduino Nano 33 BLE, a Coral Dev Board Micro, and a Coral Dev Board Mini. We compare these systems relative to their power consumption and inference time when processing 4 s EEG segments. The Coral Dev Board Mini demonstrated the fastest inference time (8.9 ms) but at the cost of high power consumption (1.7 W). The Coral Dev Board Micro balanced inference time (273 ms) with power consumption (0.6 W), while the Arduino Nano 33 BLE achieved the lowest power draw (0.1 W) but with a longer inference time (1.3 s). Our results highlight that edge AI for EEG artifact removal is possible, and likely not limited by inference time but by power consumption if long-term, battery-powered operation is required. There remains scope, and need, for further power optimization. Overall, this first-of-its-kind edge deployment of EEG processing marks a significant step towards artifact robust real-time, portable EEG monitoring solutions.

The development of some instrumentation and measurement systems poses significant challenges due to their continuous interaction with environments that are both harsh and highly dynamic. They are often described as “Untestable” because their testing is sometimes expensive, time-consuming, and infeasible. One example is oil-spill measurement systems that aim to measure the thickness of oil floating on the water surface in open water environments. In contrast to analog sensors relying on calibration functions, such integrated measurement systems use algorithms with multiple inputs to produce their measurement. Intending to facilitate the development of such systems, we shed light on virtual testing methods designed for testing Cyber-physical Systems (CPSs). CPSs are smart and autonomous systems composed of collaborating computational elements (software) that control physical entities (hardware). Effective validation and verification techniques are required to confirm their correctness. These methods were applied to test continuous controllers in the automotive domain. In this article, we review some of these testing methods and provide a framework for applying them to measurement systems that are difficult to test in real life. We provide a case study based on an oil spill measurement system that relies on multiple sensors to estimate the oil thickness in open water environments. Applying this approach creates a reduced set of test cases to be applied in real field testing reducing its cost and time.