ABSTRACT Most autonomous agents built on large language models (LLMs) focus on improving internal reasoning while assuming a fixed and complete set of primitive actions. In this work, we challenge this assumption and introduce the expansion technique, an action‐centric method that enables agents to iteratively construct new actions beyond an initial basis , forming an expanded action space . To formalize this paradigm, we present a unified framework for act‐centric autonomy that explicitly models action space evolution, distinguishing our approach from prior frameworks such as ReAct, which operate over fixed action sets. Within this framework, serves as a concrete instantiation that operationalizes dynamic action generation. We evaluate in a non‐trivial, fully observable environment—Conway's Game of Life on a grid—where the objective is to maximize the number of live cells after 300 generations. Our results show that expanding the action space leads to improved exploration and performance, demonstrating that action space completeness is insufficient and that optimizing the action set itself can significantly enhance agent effectiveness, even surpassing state‐of‐the‐art reasoning‐based approaches.

The high cost of industrial robots limits their accessibility in academic settings. This research addresses this by developing a low-cost, 3D-printable robotic arm for educational use, designed using the open-source tool FreeCAD and affordable hardware components. The robot is integrated with ROS 2 Humble and MoveIt 2, enabling motion planning and control, and includes a simulator for virtual testing and prototyping. The robot was evaluated over eight months in the Industrial Robotics and Software Architectures for Robots courses at Rey Juan Carlos University. It demonstrated durability, ease of use, and practical feasibility, making it a suitable platform for training robotics professionals. This work highlights the potential of affordable robotics to enhance education and provides a scalable, replicable solution for academic institutions.

Living with Parkinson’s Disease introduces a range of significant challenges into one’s daily life. While medical interventions exist to overcome some of these challenges, patient self-care techniques often form an essential complement to the treatments recommended by medical doctors. Knowledge on these self-care techniques often originates from those living with Parkinson’s themselves or their close caregivers, as they have the knowledge and experience required to assess self-care techniques. This so-called ‘patient knowledge’ is usually exchanged in peer meetings or discussion forums. Although vital to the Parkinson’s Disease community, this information is often difficult to access due to its unstructured format and the difficulty of navigating through online forums. We present an online tool that allows for contributing, assessing, and finally discovering Parkinson’s Disease self-care techniques. The custom discovery tool was populated with self-care knowledge by over 300 people with Parkinson’s and dozens of their carers, spanning areas such as daily well-being and using assistive equipment. Then, we invited patients to explore the discover features in a smaller scale trial. While well-received, our deployment highlighted several challenges that we further discuss in this paper. Overall, our study contributes to crowdsourced digital health solutions and provides both design and research implications to this challenging domain with a vulnerable user group.

In interdisciplinary spaces such as digital health, datasets that are complex to collect, require specialist facilities, and/or are collected with specific populations have value in a range of different sectors. In this study we collected a simulated free-living dataset, in a smart home, with 12 participants (six people with Parkinson’s, six carers). We explored their initial perceptions of the sensors through interviews and then conducted two data exploration workshops, wherein we showed participants the collected data and discussed their views on how this data, and other data relating to their Parkinson’s symptoms, might be shared across different sectors. We provide recommendations around how participants might be better engaged in considering data sharing in the early stages of research, and guidance for how research might be configured to allow for more informed data sharing practices in the future.

Continuous passive sensing of daily behavior from mobile devices has the potential to identify behavioral patterns associated with different aspects of human characteristics. This paper presents novel analytic approaches to extract and understand these behavioral patterns and their impact on predicting adaptive and maladaptive personality traits. Our machine learning analysis extends previous research by showing that both adaptive and maladaptive traits are associated with passively sensed behavior providing initial evidence for the utility of this type of data to study personality and its pathology. The analysis also suggests directions for future confirmatory studies into the underlying behavior patterns that link adaptive and maladaptive variants consistent with contemporary models of personality pathology.

Depression is a prevalent mental disorder. Current clinical and self-reported assessment methods of depression are laborious and incur recall bias. Their sporadic nature often misses severity fluctuations. Previous research highlights the potential of in-situ quantification of human behaviour using mobile sensors to augment traditional methods of depression management. In this paper, we study whether self-reported mood scores and passive smartphone and wearable sensor data could be used to classify people as depressed or non-depressed. In a longitudinal study, our participants provided daily mood (valence and arousal) scores and collected data using their smartphones and Oura Rings. We computed daily aggregations of mood, sleep, physical activity, phone usage, and GPS mobility from raw data to study the differences between the depressed and non-depressed groups and created population-level Machine Learning classification models of depression. We found statistically significant differences in GPS mobility, phone usage, sleep, physical activity and mood between depressed and non-depressed groups. An XGBoost model with daily aggregations of mood and sensor data as predictors classified participants with an accuracy of 81.43% and an Area Under the Curve of 82.31%. A Support Vector Machine using only sensor-based predictors had an accuracy of 77.06% and an Area Under the Curve of 74.25%. Our results suggest that digital biomarkers are promising in differentiating people with and without depression symptoms. This study contributes to the body of evidence supporting the role of unobtrusive mobile sensor data in understanding depression and its potential to augment depression diagnosis and monitoring.

Self-harm is a prevalent issue amongst young people, yet it is thought around 40% will never seek professional help due to stigma surrounding it. It is generally a way of coping with emotional distress and can have a range of triggers which are highly heterogeneous to the individual. In a move towards enhancing the accessibility of personalized interventions for self-harm, we undertook a three-stage study. We first conducted interviews with 4 counsellors in self-harm to understand how they clinically respond to self-harm triggers. We then ran a survey with 37 young people, to explore perceptions of mobile sensing, and current and future uses for smartphone-based interventions. Finally, we ran a workshop with 11 young people to further explore how a context-aware self-management application might be used to support them. We contribute an in-depth understanding of how triggers for self-harm might be identified and subsequently predicted and prevented using mobile-sensing technology.

Parkinson's disease (PD) is the second most common neurodegenerative disorder, impacting an estimated seven to ten million people worldwide. Measuring the symptoms and progress of the disease, and medication effectiveness is currently performed using subjective measures and visual estimation. We developed and evaluated a mobile application, STOP for tracking hand's motor symptoms, and a medication journal for recording medication intake. We followed 13 PD patients from two countries for a 1-month long real-world deployment. We found that PD patients are willing to use digital tools, such as STOP, to track their medication intake and symptoms, and are also willing to share such data with their caregivers and medical personnel to improve their own care.

We report the process used to create artefacts for self-reporting Parkinson's Disease symptoms. Our premise was that a technology-based approach would provide participants with an effective, flexible, and resilient technique. After testing four prototypes using Bluetooth, NFC, and a microcontroller we accomplished almost full compliance and high acceptance using a paper diary to track day-to-day fluctuations over 49 days. This diary is tailored to each patient's condition, does not require any handwriting, allows for implicit reminders, provides recording flexibility, and its answers can be encoded automatically. We share five design implications for future Parkinson's self-reporting artefacts: reduce participant completion demand, design to offset the effect of tremor on input, enable implicit reminders, design for positive and negative consequences of increased awareness of symptoms, and consider the effects of handwritten notes in compliance, encoding burden, and data quality.

Parkinson's disease (PD) is the second most common neurodegenerative disorder, impacting an estimated seven to ten million people worldwide. It is commonly accepted that improving medication adherence alleviates symptoms and maintains motor capabilities. Not following the medication regimen (e.g., skipping or over-medicating) may worsen side-effects, which mislead clinicians and patients. We developed and evaluated a mobile application, STOP, for screening the PD symptoms and medication intake. It contains a game for tracking the PD symptoms, and a medication journal for recording medical intake and adherence. We conducted a 1-month long real-world deployment with 13 PD patients from two countries. We found that the application medication adherence tracking provides non-bias information, and users are receptive to share such data with their care and medical personnel.

Parkinson's Disease impairs the motor, cognitive and emotional functioning of people. Clinicians do not get an accurate image of the disease because patients visit them every six months and their symptoms can change within hours. Technology has been used to tackle this problem, but most approaches disrupt people's routines or are uncomfortable to use. We aim to monitor Parkinson's in an unobtrusive, longitudinal and naturalistic way based on digital biomarkers inferred from smartphone-collected heterogeneous data. We use Parkinson's clinical scales and self-reporting of symptoms as ground truth to evaluate our methodology. Here, we present three insights we gained after tracking four people 24/7 for four months: a) the monitoring smartphone needs to be people's primary device, b) participants prefer a paper diary for symptom self-reporting, and c) social and phone interaction will be explored as digital biomarkers. We expect this approach to improve patient's quality of life and the efficiency of health services.