Neil Ireson

简介

My research interests can broadly be defined as knowledge acquisition from data. This has primarily focused on the use of Machine Learning, initially applied in the field of Natural Language Processing, developing techniques for Text Classification and Information Extraction from large-scale text repositories and Collective Intelligence from social media messages. More recent work has focused on the understanding of individual and population movements. Modelling air-travel data, to identify mobility patterns indicative of criminal activity, such as drug and human trafficking; and using mobile device sensor data to understand how contextual factors (e.g., weather, journey purpose) influence mobility, for health and well-being.

代表成果

• Kirk C, Rehman RZU, Galna B, Ranciati S, Packer E, Ireson N, Lanfranchi V, Mazzà C, Alcock L, Rochester L , Yarnall AJ et al (2026) Toward an understanding of real-world mobility in Parkinson’s: insights from enhanced contextualisation using GPS-derived location and data-driven modeling of walking speed. Frontiers in Aging Neuroscience, 18. View this article in WRRO
• Marcianò V, Cereatti A, Bertuletti S, Bonci T, Alcock L, Gazit E, Ireson N, Bevilacqua A, Mazzà C, Ciravegna F , Del Din S et al (2025) Discriminating Between Indoor and Outdoor Environments During Daily Living Activities Using Local Magnetic Field Characteristics and Machine Learning Techniques. IEEE Sensors Journal, 25(1), 1507-1515.
• Ardle RM, Wales JL, Hamilton CA, Ryan LJ, McCarthy L, Din SD, Bayat S, Tangen GG, Ireson N, Lanfranchi V , Farina N et al (2024) Feasibility and acceptability of a continuous remote activity monitoring protocol in older adult dyads: A mixed methods pilot study. Alzheimer's & dementia : the journal of the Alzheimer's Association, 20(Suppl 10).
• Ardle RM, Wales JL, Hamilton CA, Ryan LJ, McCarthy L, Din SD, Bayat S, Tangen GG, Ireson N, Lanfranchi V , Farina N et al (2024) Feasibility and acceptability of a continuous remote activity monitoring protocol in older adult dyads: A mixed methods pilot study. Alzheimer's & dementia : the journal of the Alzheimer's Association, 20(Suppl 7).
• Kirk C, Küderle A, Micó-Amigo ME, Bonci T, Paraschiv-Ionescu A, Ullrich M, Soltani A, Gazit E, Salis F, Alcock L , Aminian K et al (2024) Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device. Scientific Reports, 14(1). View this article in WRRO
• Ardle RM, Ryan LJ, McCarthy L, Wilson C, Hamilton CA, Tangen GG, Farina N, Ireson N, Lanfranchi V, Hicks B & Bayat S (2023) Investigating the social and environmental contexts of habitual walking ACTIVities in older adult DYADs: A mixed methods protocol for the ActivDyad study. Alzheimer's & Dementia, 19(S23).
• Ardle RM, Ryan LJ, McCarthy L, Wilson C, Hamilton CA, Tangen GG, Farina N, Ireson N, Lanfranchi V, Hicks B & Bayat S (2023) Investigating the social and environmental contexts of habitual walking ACTIVities in older adult DYADs: A mixed methods protocol for the ActivDyad study. Alzheimer's & Dementia, 19(S11).
• Packer E, Debelle H, Bailey HGB, Ciravegna F, Ireson N, Evers J, Niessen M, Shi JQ, Yarnall AJ, Rochester L , Alcock L et al (2023) Translating digital healthcare to enhance clinical management: a protocol for an observational study using a digital health technology system to monitor medication adherence and its effect on mobility in people with Parkinson’s. BMJ Open, 13. View this article in WRRO
• Debelle H, Packer E, Beales E, Bailey HGB, Mc Ardle R, Brown P, Hunter H, Ciravegna F, Ireson N, Evers J , Niessen M et al (2023) Feasibility and usability of a digital health technology system to monitor mobility and assess medication adherence in mild-to-moderate Parkinson's disease. Frontiers in Neurology, 14. View this article in WRRO
• Marcianò V, Bertuletti S, Bonci T, Mazzà C, Ireson N, Ciravegna F, Del Din S, Gazit E & Cereatti A (2022) A deep learning model to discern indoor from outdoor environments based on data recorded by a tri-axial digital magnetic sensor. Gait & Posture, 97, 5-5.