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Arshad Sher

Senior Lecturer

School of Science & Technology

Staff Group(s)
Computer Science

Role

Dr. Arshad Sher holds a Ph.D. in Computer Science and combines expertise in wearable computing, clinical automation, and machine learning to pioneer AI-driven healthcare solutions. He serves as the Module Leader for Advanced Analysis and Design and is a Fellow of the Higher Education Academy (FHEA).

At the heart of his research is the utilization of wearable sensors to automate clinical functional assessments. By applying classification and detection models to mobile IMU data, his systems can:

1. Automate the scoring of standard metrics, such as the Chair Sit-to-Stand Test, using smartphone technology.

2. Conduct root cause analysis for conditions like Parkinson’s disease and Stroke,

3. Perform personalized, environment-aware gait analysis to characterize walking stability in real-world settings.

Dr. Sher's work aims to develop robust, fully automated pipelines that transform how patient mobility is monitored and analyzed outside of laboratory environments.

I am actively looking for PhD students.

Career overview

Dr. Arshad Sher’s academic career is built on a foundation of rigorous research in sensor networks and intelligent systems, culminating in a Ph.D. from Aberystwyth University. Before his current appointment at Nottingham Trent University, he honed his expertise as a Postdoctoral Research Associate and Associate Lecturer, where he bridged the gap between computer science and clinical rehabilitation. His research trajectory has evolved from significant early contributions in underwater wireless sensor networks (UWSNs) and IoT routing protocols to his current specialization in wearable healthcare technologies and gait analysis. A Fellow of the Higher Education Academy (FHEA), Dr. Sher actively contributes to the academic community as a module leader, MSc, PhD supervisor, Journal Invited Reviewer and Technical Programme Committee member for international conferences such as IEEE-EMBS BHI.

Research areas

Dr. Arshad Sher’s research interest is mainly in the area of wearable computing, machine learning, and the Internet of Things (IoT). Specific areas of interest include automated gait analysis, clinical functional assessment, smartphone sensing, IMU sensors, stroke rehabilitation, Parkinson’s disease monitoring, and underwater wireless sensor networks (UWSN).

His works have been recognized internationally for significant contributions to the application of computational intelligence techniques in healthcare automation and wireless sensor routing protocols. His strong technical and collaborative skills, demonstrated through the management of multidisciplinary data collection protocols, have helped him to meet targeted research objectives. Furthermore, his extensive background in the supervision and teaching of undergraduate and postgraduate students, including module leadership in advanced system programming, advanced analysis and design, applied AI and Data Mining have helped him to contribute to the field of research.

His current research focuses on the identification of functional mobility changes in patients suffering from Parkinson’s disease and Stroke. Accurate identification of these changes through the utilization of unobtrusive smartphone sensing and AI classification models enables the automation of standard clinical metrics and supports root cause analysis for remote health monitoring.

Opportunities to carry out postgraduate research towards a PhD exist in all of the areas identified above.

External activity

  • Technical Programme Committee—iCoMET (2024 - Present)
  • Technical Programme Committee – UKCI 2021: The 20th UK Workshop on Computational Intelligence. Aberystwyth, UK, 8-10 September 2021.
  • Technical Programme Committee – ICICT 2019 The 7th International Conference on Information and Communication Technology, Kuala Lumpur, Malaysia, 20-26 July 2019.
  • Invited Reviewer -
  • Concurrency and Computation: Practice and Experience ·
  • Journal of King Saud University – Computer and Information Science (Elsevier)
  • IEEE Access
  • Frontiers
  • The Journal of Health Reports and Technology

Publications

Sher, A., Rashid, M., Lotfi, A. et al. Cycle Metrics and Strategy Detection for Automated Chair Sit-to-Stand Test Analysis Employing a Single Smartphone.  Ann Biomed Eng (2025). https://doi.org/10.1007/s10439-025-03943-4

Rashid, M.; Sher, A.; Povina, F.V.; Akanyeti, O. AI-Driven Adaptive Segmentation of Timed Up and Go Test Phases Using a Smartphone. Electronics 2025, 14, 4650. https://doi.org/10.3390/electronics14234650

Sher, A., & Akanyeti, O. (2024). Minimum data sampling requirements for accurate detection of terrain-induced gait alterations change with mobile sensor position. Pervasive and Mobile Computing, 105, 101994.

Sher, A., Bunker, M. T., & Akanyeti, O. (2023). Towards personalized environment‐aware outdoor gait analysis using a smartphone. Expert Systems, 40(5), e13130.

Sher, A., Langford, D., Villagra, F., & Akanyeti, O. (2022, September). Automatic scoring of chair sit-to-stand test using a smartphone. In UK Workshop on Computational Intelligence (pp. 170-180). Cham: Springer Nature Switzerland.

Bunker, M. T., Sher, A., Akpokodje, V., Villagra, F., Parthaláin, N. M., & Akanyeti, O. (2021, September). Towards fuzzy context-aware automatic gait assessments in free-living environments. In Uk workshop on computational intelligence (pp. 463-474). Cham: Springer International Publishing.

Khalid, R., Javaid, N., Rahim, M. H., Aslam, S., & Sher, A. (2019). Fuzzy energy management controller and scheduler for smart homes.  Sustainable Computing: Informatics and Systems, 21, 103-118.

Javaid, N., Sher, A., Nasir, H., & Guizani, N. (2018). Intelligence in IoT-based 5G networks: Opportunities and challenges. IEEE Communications Magazine, 56(10), 94-100.

Ali, B., Sher, A., Javaid, N., Islam, S. U., Aurangzeb, K., & Haider, S. I. (2018). Retransmission avoidance for reliable data delivery in underwater WSNs. Sensors, 18(1), 149.

For more publications, refer to Google Scholar