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Recent Submissions

A multimodal bracelet to acquire muscular activity and gyroscopic data to study sensor fusion for intent detection
(MDPI, 2024) Andreas, Daniel; Hou, Zhongshi; Tabak, Mohamad Obada; Dwivedi, Anany; Beckerle, Philipp
Researchers have attempted to control robotic hands and prostheses through biosignals but could not match the human hand. Surface electromyography records electrical muscle activity using non-invasive electrodes and has been the primary method in most studies. While surface electromyography-based hand motion decoding shows promise, it has not yet met the requirements for reliable use. Combining different sensing modalities has been shown to improve hand gesture classification accuracy. This work introduces a multimodal bracelet that integrates a 24-channel force myography system with six commercial surface electromyography sensors, each containing a six-axis inertial measurement unit. The device’s functionality was tested by acquiring muscular activity with the proposed device from five participants performing five different gestures in a random order. A random forest model was then used to classify the performed gestures from the acquired signal. The results confirmed the device’s functionality, making it suitable to study sensor fusion for intent detection in future studies. The results showed that combining all modalities yielded the highest classification accuracies across all participants, reaching (Formula presented.) on average, effectively reducing misclassifications by 37% and 22% compared to using surface electromyography and force myography individually as input signals, respectively. This demonstrates the potential benefits of sensor fusion for more robust and accurate hand gesture classification and paves the way for advanced control of robotic and prosthetic hands.
Topological and dynamic characteristics in forced anisotropic magnetohydrodynamic turbulence
(AIP Publishing, 2026) Gao, Kai; Jiang, Bin; Li, Cheng; Yang, Yan; Zhou, Kangcheng; Matthaeus, William H.; Oughton, Sean; Wan, Minping
The invariants of the velocity gradient tensor in turbulence offer a compact description of local kinematics and flow topology. For incompressible magnetohydrodynamic turbulence, analysis of the second and third invariants (Q, R) of the velocity gradient tensor clarifies how coherent structures are organized and evolve. Extending the same analysis to the magnetic field gradient tensor provides additional information on the dynamics. In this study, pseudo-spectral simulation is used to obtain the velocity and magnetic field of the turbulent flow, and analysis of the flow field is conducted through joint probability density functions (PDFs) of the invariants. Furthermore, we explore the influence of the external mean magnetic field strength, B 0. The results show that when an external magnetic field is present, the Q–R joint PDF no longer maintains the familiar teardrop distribution for the velocity field, and the flow field structure tends to be two-dimensional with increasing B 0. For the fluctuation magnetic field, the Q–R joint PDF takes on a “cigar” shape that becomes more elongated as B 0 increases. Moreover, as the strength of the external mean magnetic field increases, the turbulence exhibits enhanced small-scale dissipation and localization, accompanied by a reduction in the effective dimensionality of the system toward a quasi-two-dimensional regime.
Anomaly detection for evolving maritime trajectories with continual learning
(Springer Nature, 2026) Julian, Jack; Koh, Yun Sing; Bifet, Albert
Anomaly detection in live trajectory data is a critical task for ensuring safety, security, and legality in global transport. Traditional anomaly detection methods often struggle with dynamic and evolving trajectory patterns, especially as systems must adapt to new scenarios over time due to increased traffic, geopolitical events, or global warming. We propose a continual learning approach to detect anomalous activity in moving vessels. Unlike conventional static models, our method leverages continual learning to enable the model to learn from new data continuously and recognise specific behaviours dependent on position and recent movements. We implement an adapter-based framework, Continual Learning for AIS Anomalies (CLAISA), that adapts to shifting behavioural environments in transportation, ensuring the system can identify novel and evolving patterns of anomalies, such as deviations from expected routes, irregular speed changes, or unusual local movements. Evaluations on synthetic maritime trajectory datasets spanning sparsely populated waters and heavily trafficked shipping lanes demonstrate that CLAISA achieves up to a decrease in error for trajectory forecasting and consistently outperforms benchmark methods in anomaly detection on synthetically generated datasets.
Integrating agentic artificial intelligence into pasture-based dairy systems: Applications, governance, and future directions
(Elsevier, 2026) Eastwood, Callum; Lim, Nick Jin Sean; Durie, Rachel; Dela Rue, Brian; Bifet, Albert; Reed, Charlotte
Digitalization and artificial intelligence (AI) provide opportunities for improved management of pasture-based dairy systems. Agentic AI, where autonomous systems can perceive and act independently of humans, are potentially transformative. This mini-review explores the potential use of agentic AI to address key challenges in pasture-based dairy systems. Applications include autonomous grazing animal health prediction, environmental modeling, and virtual assistants. Agentic AI can integrate multiple data sources to support real-time, farm-specific decisions. It also presents opportunities for employee training, enhanced advisory services, and digital twin modeling. However, deployment of agentic AI introduces governance, ethical, and socio-technical considerations. Issues of data ownership, transparency, explainability, and trust must be addressed. The experiential and tacit knowledge of farmers must be integrated into AI systems through hybrid intelligence (human and AI). Oversight models ranging from Human-in-the-Loop to Human-in-Command are necessary to ensure safe and responsible use. Future research should focus not only on technical AI development, but farmer-centered design, robust assurance frameworks, and inclusive and responsible innovation ecosystems that align technological progress with dairy sector, civil society, values, and needs.
An agentic system for LLM-driven public transportation analytics: A practical application and case study in Salvador-Brazil
(ACM, 2026) Borges, Lucas T.; Liu, Fei T.; Rios, Tatiane; Ferreira, Marcos V.; Carmo, Clovis; Coimbra, Danilo B.; Nery, Jorge; Souza, Matheus; Garcia, Noe O.; Bifet, Albert; Rios, Ricardo
Public transportation agencies gather vast and heterogeneous datasets, yet their decisions still depend largely on manual queries and fragmented analyses. To address this gap, we introduce SUNTInsight, an agentic system that combines large language models, machine learning, and visual analytics to strengthen human decision-making. Through a seamless workflow, free-form prompts are automatically converted into SQL queries, relevant data are retrieved and processed, and an LLM, supported by visual interfaces, interprets the results to produce actionable findings. Applied to a case study in Salvador-Brazil, SUNTInsight revealed spatial heterogeneity, identified high-demand segments, and recommended targeted operational strategies, such as short turns and headway control, instead of broad fleet expansions. The implementation follows the principle of least privilege and runs generated code in isolated execution, mitigating risk while keeping humans in control.