Clinical Application and Challenges of Wearable Sensors in Freezing of Gait Monitoring in Parkinson’s Disease
DOI:
https://doi.org/10.54097/pd8yka19Keywords:
Wearable sensors, Parkinson’s disease, Freezing of gait monitoring.Abstract
Parkinson’s disease (PD) is the second common neurodegenerative disorder in the world. Freezing of Gait (FOG) is one of the most harmful symptoms of PD. Traditional assessment methods, such as questionnaire surveys and non-wearable monitoring devices are limited by patients’ subjective cognitive, clinicians’ experience, and the suddenness and fluctuations of FOG. In recent years, wearable sensors have become a new way for real-time FOG monitoring in various scenarios for a long term. This study reviews the characteristics of PD-FOG and its limitations of traditional assessment. It explores the advantages and necessities of wearable sensors in FOG real-time monitoring. This study also analyzes three of the common sensors in a broad vision, which present both values and challenges of them. In addition, current challenges in sensitivity, specifically, stability, security and disconnection of wearable monitoring technology are discussed. Overall, FOG monitoring based on wearable sensors has had a range of achievements, but further developments are needed to improve its performance and comfort before a perfect clinical transformation.
Downloads
References
[1] Factor SA. The clinical spectrum of freezing of gait in atypical parkinsonism. Mov Disord, 2008, 23: 431-438.
[2] van der Heide A, Meinders MJ, Speckens AEM, Peerbolte TF, Bloem BR, Helmich RC. Stress and Mindfulness in Parkinson's Disease: Clinical Effects and Potential Underlying Mechanisms. Mov Disord, 2021, 36 (1): 64-70.
[3] Smid A, Elting JWJ, van Dijk JMC, et al. Intraoperative Quantification of MDS-UPDRS Tremor Measurements Using 3D Accelerometry: A Pilot Study. J Clin Med, 2022, 11 (9): 2275.
[4] Moreau C, Rouaud T, Grabli D, et al. Overview on wearable sensors for the management of Parkinson's disease. NPJ Parkinsons Dis, 2023, 9 (1): 153.
[5] Nutt JG, Bloem BR, Giladi N, Hallett M, Horak FB, Nieuwboer A. Freezing of gait: moving forward on a mysterious clinical phenomenon. Lancet Neurol, 2011, 10 (8): 734-744.
[6] Bloem BR, Hausdorff JM, Visser JE, Giladi N. Falls and freezing of gait in Parkinson's disease: a review of two interconnected, episodic phenomena. Mov Disord, 2004, 19 (8): 871-884.
[7] Shackleford MR, Mishra V, Mari Z. Levodopa-Carbidopa Intestinal Gel may improve treatment-resistant freezing of gait in Parkinson's disease. Clin Park Relat Disord, 2022.
[8] Hulzinga F, Nieuwboer A, Dijkstra BW, et al. The New Freezing of Gait Questionnaire: Unsuitable as an Outcome in Clinical Trials? Mov Disord Clin Pract, 2020, 7 (2): 199-205.
[9] Wang M, Zhang W, Zang W. Repetitive transcranial magnetic stimulation improves cognition, depression, and walking ability in patients with Parkinson's disease: a meta-analysis. BMC Neurol, 2024, 24 (1): 490.
[10] Ji Y, Yang C, Pang X, et al. Repetitive transcranial magnetic stimulation in Alzheimer's disease: effects on neural and synaptic rehabilitation. Neural Regen Res, 2025, 20 (2): 326-342.
[11] L. Wang, Y. Sun, Q. Li and T. Liu. Estimation of Step Length and Gait Asymmetry Using Wearable Inertial Sensors. IEEE Sensors Journal, 2018, 18 (9): 3844-3851.
[12] D. Chen, Y. Cai and M. -C. Huang. Customizable Pressure Sensor Array: Design and Evaluation. IEEE Sensors Journal, 2018, 18 (15): 6337-6344.
[13] H. Chen, Z. Wang, L. Meng, W. Qin, J. Wu, J. Liu. Non disturbance gait signal acquisition insole for daily monitoring. 2023 IEEE 19th International Conference on Body Sensor Networks (BSN), Boston, 2023.
[14] Moore A, Li J, Contag CH, Currano LJ, Pyles CO, Hinkle DA, Patil VS. Wearable Surface Electromyography System to Predict Freeze of Gait in Parkinson's Disease Patients. Sensors (Basel), 2024, 24 (23): 7853.
[15] N. Xu, N. Xu, C. Wang, L. Peng, X-H. Zhou, J. Chen, Z. Cheng. A Double-Hurdle Quantification Model for Freezing of Gait of Parkinson's Patients. IEEE Transactions on Biomedical Engineering, 2024, 71 (10): 2936-2947.
[16] Hemant Ghayvat, Muhammad Awais, Rebakah Geddam, Mohammad Tabrez Quasim, Sunder Ali Khowaja, Kapal Dev. AiCareGaitRehabilitation: Multi-modalities sensor data fusion for AI-IoT enabled realtime electrical stimulation device for pre-Fog and post-Fog to person with Parkinson’s Disease. Information Fusion, 2025.
[17] Sarah J. Conklin, Helen Meira Cavalcanti, Lorena Rosa S. Almeida, Virendra Mishra, Jamary Oliveira-Filho, Zoltan Mari, Merrill R. Landers, Jason K. Longhurst. Identifying gait characteristics associated with freezing of gait in Parkinson’s disease: An analysis of on and off medication states. Gait & Posture, 2025, 122: 225-231.
[18] Brittany Intzandt, Eric N. Beck, Carolina R.A. Silveira. The effects of exercise on cognition and gait in Parkinson’s disease: A scoping review. Neuroscience & Biobehavioral Reviews, 2018, 95: 136-169.
[19] Ali Haddadi Esfahani, Oliver Maye, Max Frohberg, Maria Speh, Micheal Jöbges, Peter Langendörfer. Real Time Detection of Freezing of Gait of Parkinson Patients based on Machine Learning Running on a Body Worn Device. Procedia Computer Science, 2024, 239: 177-184.
[20] L. Li, X. Hu, H. Wu, X. Zhao, R. Guo and M. Sun. A Review of Wearable Inertial Sensor Gait Modeling. 2025 International Conference on Mechatronics, Robotics, and Artificial Intelligence (MRAI), Jinan, China, 2025.
[21] M. Bachlin, M. Plotnik, D. Roggen, I. Maidan, JM. Hausdorff, N. Giladi. Wearable Assistant for Parkinson’s Disease Patients With the Freezing of Gait Symptom. IEEE Transactions on Information Technology in Biomedicine, 2010, 14 (2): 436-446.
[22] P. Tahafchi, J. W. Judy. Freezing-of-Gait Detection Using Wearable Sensor Technology and Possibilistic K-Nearest-Neighbor Algorithm. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 2019.
[23] Paula Delgado-Santos, Giuseppe Stragapede, Ruben Tolosana, Richard Guest, Farzin Deravi, and Ruben Vera-Rodriguez. A Survey of Privacy Vulnerabilities of Mobile Device Sensors. ACM Comput, 2022, 54 (11): 1-30.
[24] T. T. Ngo, M. A. R. Ahad, A. D. Antar, M. Ahmed, D. Muramatsu, Y. Makihara, Y. Yagi, S. Inoue, T. Hossain, and Y. Hattori. OU-ISIR wearable sensor-based gait challenge: Age and gender. Proc. International Conference on Biometrics, Crete, Greece, 2019.
[25] R. A. Razali and N. Jamil. A Quick Review of Security Issues in Telemedicine. 8th International Conference on Information Technology and Multimedia (ICIMU), Selangor, Malaysia, 2020.
[26] S. P. Yuninda, S. Aga Pasma and T. Mantoro. Patient Data Security in Telemedicine Services from Data Misuse in Health Practice. 2022 IEEE 8th International Conference on Computing, Engineering and Design (ICCED), Sukabumi, Indonesia, 2022.
[27] F. Vercesi, L. Corso, G. Allegato, G. Gattere, L. Guerinoni, C. Valzasina. Thelma-double: A New Technology Platform for Manufacturing of High-Performance Mems Inertial Sensors. 2022 IEEE 35th International Conference on Micro Electro Mechanical Systems Conference (MEMS), Tokyo, Japan, 2022.
[28] H. C. Nguyen, S. Debbarma and S. Bhadra. Flexible Fabric Electrodes Integrated with Mouthguard for Electroocoulogram Measurement. 2023 IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS), Boston, MA, USA, 2023.
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

