Sinziana Mazilu
Skip Abstract Section
Abstract
People with Parkinson’s disease (PD) suffer from declining mobility capabilities, which cause a prevalent risk of falling. Commonly, short periods of motor blocks occur during walking, known as freezing of gait (FoG). To slow the progressive decline of motor abilities, people with PD usually undertake stationary motor-training exercises in the clinics or supervised by physiotherapists. We present a wearable system for the support of people with PD and FoG. The system is designed for independent use. It enables motor training and gait assistance at home and other unsupervised environments. The system consists of three components. First, FoG episodes are detected in real time using wearable inertial sensors and a smartphone as the processing unit. Second, a feedback mechanism triggers a rhythmic auditory signal to the user to alleviate freeze episodes in an assistive mode. Third, the smartphone-based application features support for training exercises. Moreover, the system allows unobtrusive and long-term monitoring of the user’s clinical condition by transmitting sensing data and statistics to a telemedicine service.
We investigate the at-home acceptance of the wearable system in a study with nine PD subjects. Participants deployed and used the system on their own, without any clinical support, at their homes during three protocol sessions in 1 week. Users’ feedback suggests an overall positive attitude toward adopting and using the system in their daily life, indicating that the system supports them in improving their gait. Further, in a data-driven analysis with sensing data from five participants, we study whether there is an observable effect on the gait during use of the system. In three out of five subjects, we observed a decrease in FoG duration distributions over the protocol days during gait-training exercises. Moreover, sensing data-driven analysis shows a decrease in FoG duration and FoG number in four out of five participants when they use the system as a gait-assistive tool during normal daily life activities at home.
- H. Alemdar and C. Ersoy. 2010. Wireless sensor networks for healthcare: A survey. Computer Networks 54, 2688--2710. Google Scholar
Digital Library
- N. E. Allen, C. G. Canning, C. Sherrington, S. R. Lord, M. D. Latt, J. C. Close, S. D. O’Rourke, S. M. Murray, and V. S. Fung. 2010. The effects of an exercise program on fall risk factors in people with PD: A randomized controlled trial. Movement Disorders 25, 1217--1225.Google ScholarCross Ref
- N. E. Allen, C. Sherrington, G. D. Suriyarachchi, S. S. Paul, J. Song, and C. G. Canning. 2012. Exercise and motor training in people with Parkinson’s disease: A systematic review of participant characteristics, intervention delivery, retention rates, adherence, and adverse event in clinical trials. Parkinsons Disease 2012, Article No. 854328.Google Scholar
- M. Bächlin, M. Plotnik, D. Roggen, I. Maidan, J. M. Hausdorff, N. Giladi, and G. Tröster. 2010. Wearable assistant for Parkinson’s disease patients with the freezing of gait symptom. IEEE Transactions on Information Technology in Biomedicine 14, 436--446. Google ScholarDigital Library
- B. R. Bloem, J. M. Hausdorff, J. E. Visser, and N. Giladi. 2004. Falls and freezing of gait in Parkinson’s disease: A review of two interconnected, episodic phenomena. Movement Disorders 19, 871--884.Google ScholarCross Ref
- A. Bulling, U. Blanke, and B. Schiele. 2014. A tutorial on human activity recognition using body-worn inertial sensors. ACM Computing Surveys 46, 1--33. Google ScholarDigital Library
- Y. Choi, A. S. Ralhan, and S. Ko. 2011. A study on machine learning algorithms for fall detection and movement classification. In Proceedings of the 2011 International Conference on Information Science and Applications (ICISA).Google Scholar
- B. T. Cole, S. H. Roy, and S. H. Nawab. 2011. Detecting freezing of gait during unscripted and unconstrained activity. In Proceedings of the Engineering in Medicine and Biology Society Conference (EMBC). 5649--5652.Google Scholar
- L. Demers, R. Weiss-Lambrou, and B. Ska. 1996. Development of the Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST). Assistive Technologies 8, 3--13.Google ScholarCross Ref
- M. Djurić-Jovičić, N. S. Jovičić, I. Milovanović, S. Radovanović, N. Kresojević, and M. B. Popović. 2010. Classification of walking patterns in Parkinson’s disease patients based on inertial sensor data. In Proceedings of the 10th Symposium on Neural Network Applications in Electrical Engineering. 3--6.Google Scholar
- S. Donovan, C. Lim, N. Diaz, N. Browner, P. Rose, L. R. Sudarsky, D. Tarsy, S. Fahn, and D. K. Simon. 2011. Laserlight cues for gait freezing in Parkinson’s disease: An open-label study. Parkinsonism and Related Disorders 17, 240--245.Google ScholarCross Ref
- E. R. Dorsey, R. Constantinescu, J. P. Thompson, K. M. Biglan, R. G. Holloway, K. Kieburtz, F. J. Marshall, B. M. Ravina, G. Schifitto, A. Siderowf, and C. M. Tanner. 2007. Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030. Neurology 68, 384--386.Google ScholarCross Ref
- G. Frazzitta, G. Bertotti, D. Uccellini, and R. Maestri. 2010. Parkinson’s disease rehabilitation: A pilot study with 1 year follow up. Movement Disorders 25, 1762--1763.Google ScholarCross Ref
- C. Geh, M. Beauchamp, P. Crocker, and M. Carpenter. 2011. Assessed and distressed: White coat effects on clinical balance performance. Journal of Psychosomatic Research 70, 45--51.Google ScholarCross Ref
- N. Giladi and J. M Hausdorff. 2006. The role of mental function in the pathogenesis of freezing of gait in Parkinson’s disease. Journal of the Neurological Sciences 248, 173--176.Google ScholarCross Ref
- N. Giladi, J. Tal, T. Azulay, O. Rascol, D. J. Brooks, E. Melamed, W. Oertel, W. H. Poewe, F. Stocchi, and E. Tolosa. 2009. Validation of the freezing of gait questionnaire in patients with Parkinson’s disease. Movement Disorders 24, 655--661.Google ScholarCross Ref
- C. G. Goetz, B. C. Tilley, S. R. Shaftman, G. T. Stebbins, S. Fahn, P. Martinez-Martin, W. Poewe, C. Sampaio, M. B. Stern, et al. 2008. Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): Scale presentation and clinimetric testing results. Movement Disorders 23, 2129--2170.Google ScholarCross Ref
- P. Gray and K. Hildebrand. 2000. Fall risk factors in Parkinson’s disease. Journal of Neuroscience Nursing 32, 4, 222--228.Google ScholarCross Ref
- E. Grönvall and N. Verdezoto. 2013. Beyond self-monitoring: Understanding non-functional aspects of home-based healthcare technology. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp). Google ScholarDigital Library
- A. M. Handojoseno, J. M. Shine, T. N. Nguyen, Y. Tran, S. J. Lewis, and H. T. Nguyen. 2012. The detection of freezing of gait in Parkinson’s disease patients using EEG signals based on Wavelet decomposition. In Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 69--72.Google Scholar
- J. M. Hausdorff, J. Schaafsma, Y. Balash, A. L. Bartels, T. Gurevich, and N. Giladi. 2003. Impaired regulation of stride variability in Parkinson’s disease subjects with freezing of gait. Experimental Brain Research 149, 187--194.Google ScholarCross Ref
- J. M. Hausdorff, J. Lowenthal, T. Herman, L. Gruendligger, C. Peretz, and N. Giladi. 2007. Rhythmic auditory stimulation modulates gait variability in Parkinson’s disease. European Journal of Neuroscience 26, 8, 2369--2375.Google ScholarCross Ref
- M. Hoehn and M. Yahr. 1967. Parkinsonism: Onset, progression and mortality. Neurology 17, 5, 427--442.Google ScholarCross Ref
- E. Jovanov, E. Wang, L. Verhagen, M. Fredrickson, and R. Fratangelo. 2009. deFOG: A real time system for detection and unfreezing of gait of Parkinson’s patients. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).Google Scholar
- I. Lim, C. van Wegen, E. de Goede, M. Deutekom, A. Nieuwboer, A. Willems, D. Jones, L. Rochester, and G. Kwakkel. 2005. Effects of external rhythmical cueing on gait in patients with Parkinson’s disease: A systematic review. Clinical Rehabilitation 19, 695--713.Google ScholarCross Ref
- S. Mazilu, U. Blanke, M. Hardegger, G. Tröster, E. Gazit, and J. M. Hausdorff. 2014a. GaitAssist: A daily-life support and training system for Parkinson’s disease patients with freezing of gait. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Google ScholarDigital Library
- S. Mazilu, U. Blanke, D. Roggen, G. Tröster, E. Gazit, and J. M. Hausdorff. 2013a. Engineers meet clinicians: Augmenting Parkinson’s disease patients to gather information for gait rehabilitation. In Proceedings of the 4th Augmented Human International Conference. Google ScholarDigital Library
- S. Mazilu, A. Calatroni, E. Gazit, D. Roggen, J. M. Hausdorff, and G. Tröster. 2013b. Feature learning for detection and prediction of freezing of gait in Parkinson’s disease. In Machine Learning and Data Mining in Pattern Recognition. Lecture Notes in Computer Science, Vol. 7988. Springer, 144--158. Google ScholarDigital Library
- S. Mazilu, U. Blanke, M. Hardegger, G. Tröster, E. Gazit, M. Dorfman, and J. M. Hausdorff. 2014b. GaitAssist: A wearable assistant for gait training and rehabilitation in Parkinson’s disease. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).Google Scholar
- S. Mazilu, M. Hardegger, Z. Zhu, D. Roggen, G. Tröster, M. Plotnik, and J. M. Hausdorff. 2012. Online detection of freezing of gait with smartphones and machine learning techniques. In Proceedings of the 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth).Google Scholar
- S. T. Moore, H. G. MacDougall, and W. G. Ondo. 2008. Ambulatory monitoring of freezing of gait in Parkinson’s disease. Journal of Neuroscience Methods 167, 340--348.Google ScholarCross Ref
- W. Muangpaisan, A. Mathews, H. Hori, and D. Seidel. 2011. A systematic review of the worldwide prevalence and incidence of Parkinson’s disease. Journal of the Medical Association of Thailand 94, 749--755.Google Scholar
- K. Niazmand, K. Tonn, Y. Zhao, U. M. Fietzek, F. Schroeteler, K. Ziegler, A. O. Ceballos-Baumann, and T. C. Lueth. 2011. Freezing of gait detection in Parkinson’s disease using accelerometer based smart clothes. In Proceedings of the Biomedical Circuits and Systems Conference (BioCAS).Google Scholar
- A. Nieuwboer. 2008. Cueing for freezing of gait in patients with Parkinson’s disease: A rehabilitation perspective. Movement Disorders 23, 475--481.Google ScholarCross Ref
- A. Nieuwboer and N. Giladi. 2008. The challenge of evaluating freezing of gait in Parkinson’s disease. Brain Journal on Neurosurgery 22, S16--S18.Google ScholarCross Ref
- A. Nieuwboer, G. Kwakkel, L. Rochester, D. Jones, E. van Wegen, A. M. Willems, F. Chavret, V. Hetherington, K. Baker, and I. Lim. 2007. Cueing training in the home improves gait-related mobility in Parkinson’s disease: The RESCUE trial. Journal of Neurology, Neurosurgery and Psychiatry 78, 2, 134--140.Google ScholarCross Ref
- J. G Nutt, B. R. Bloem, N. Giladi, M. Hallett, F. B. Horak, and A. Nieuwboer. 2011. Freezing of gait: Moving forward on a mysterious clinical phenomenon. Lancet Neurolology 10, 734--744.Google ScholarCross Ref
- Y. Okuma and N. Yanagisawa. 2008. The clinical spectrum of freezing of gait in Parkinson’s disease. Movement Disorders 23, 426--430.Google ScholarCross Ref
- S. Patel, H. Park, P. Bonato, L. Chan, and M. Rodgers. 2012. A review of wearable sensors and systems with application in rehabilitation. Journal of Neuroenginnering and Rehabilitation 9, 21.Google ScholarCross Ref
- E. Pelosin, L. Avanzino, M. Bove, P. Stramesi, A. Nieuwboer, and G. Abbruzzese. 2010. Action observation improves freezing of gait in patients with Parkinson’s disease. Neurorehabilitation and Neural Repair 24, 8, 746--752.Google ScholarCross Ref
- C. Ramaker, J. Marinus, A. M. Stiggelbout, and B. J. Van Hilten. 2002. Systematic evaluation of rating scales for impairment and disability in Parkinson’s disease. Movement Disorders 17, 5, 867--876.Google ScholarCross Ref
- T. C. Rubinstein, N. Giladi, and J. M. Hausdorff. 2002. The power of cueing to circumvent dopamine deficits: A review of physical therapy treatment of gait disturbances in Parkinson’s disease. Movement Disorders 17, 1148--1160.Google ScholarCross Ref
- J. D. Schaafsma, Y. Balash, T. Gurevich, A. L. Bartels, J. M. Hausdorff, and N. Giladi. 2003. Characterization of freezing of gait subtypes and the response of each to levodopa in Parkinson’s disease. European Journal of Neurology 10, 391--398.Google ScholarCross Ref
- D. Schuler and A. Namioka (Eds.). 1993. Participatory Design: Principles and Practices. Routledge. Google ScholarDigital Library
- A. M. Sponselee, B. Schouten, D. Bouwhuis, and C. Willems. 2008. Smart home technology for the elderly: Perceptions of multidisciplinary stakeholders. In Constructing Ambient Intelligence. Communications in Computer and Information Science, Vol. 11. Springer, 314--326.Google Scholar
- R. Steele, A. Lo, C. Secombe, and Y. K. Wong. 2009. Elderly persons’ perception and acceptance of using wireless sensor networks to assist healthcare. International Journal of Medical Informatics 78, 788--801.Google ScholarCross Ref
- D. Tan, M. Danoudis, J. McGinley, and M. E. Morris. 2012. Relationships between motor aspects of gait impairments and activity limitations in people with Parkinson’s disease: A systematic review. Parkinsonism Related Disorders 18, 117--124.Google ScholarCross Ref
- M. H. Thaut, G. C. McIntosh, R. A. Rice, R. R. Miller, J. Rathbun, and J. M. Brault. 1996. Rhythmic auditory stimulation in gait training for Parkinson’s disease patients. Movement Disorders 11, 193--200.Google ScholarCross Ref
- C. L. Tomlinson, S. Patel, C. Meek, C. E. Clarke, R. Stowe, L. Shah, C. M. Sackley, K. H. Deane, C. P. Herd, K. Wheatley, and N. Ives. 2012. Physiotherapy versus placebo or no intervention in Parkinson’s disease. Cochrane Database of Systematic Reviews 8, CD002817.Google ScholarCross Ref
- E. Tripoliti, A. Tzallas, M. Tsipouras, G. Rigas, P. Bougia, M. Leontiou, S. Konitsiotis, M. Chondrogiorgi, S. Tsouli, and D. I. Fotiadis. 2013. Automatic detection of freezing of gait events in patients with Parkinson’s disease. Computer Methods and Programs in Biomedicine 110, 1, 12--26. Google ScholarDigital Library
- Y. Zhao, K. Tonn, K. Niazmand, U. M. Fietzek, L. T. D’Angelo, A. Ceballos-Baumann, and T. C. Lueth. 2012. Online FOG identification in Parkinson’s disease with a time-frequency combined algorithm. In Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI).Google Scholar
- K. Ziegler, F. Schroeteler, A. Ceballos-Baumann, and U. M. Fietzek. 2010. A new rating instrument to assess festination and freezing gait in Parkinsonian patients. Movement Disorders 25, 1012--1018.Google ScholarCross Ref
Index Terms
- A Wearable Assistant for Gait Training for Parkinson’s Disease with Freezing of Gait in Out-of-the-Lab Environments
Recommendations
GaitAssist: a daily-life support and training system for parkinson's disease patients with freezing of gait
CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing SystemsPatients with Parkinson's disease often experience freezing of gait, which bears a high risk of falling, a prevalent cause for morbidity and mortality. In this work we present GaitAssist, a wearable system for freezing of gait support in daily life. The ...
Gait parameters change prior to freezing in Parkinson's disease: a data-driven study with wearable inertial units
BodyNets '15: Proceedings of the 10th EAI International Conference on Body Area NetworksFreezing of gait (FoG) is a motor impairment among patients with advanced Parkinson's disease which is associated with falls and has a negative impact on a patient's quality of life. Wearable systems have been developed to detect FoG and to help ...
Wearable assistant for Parkinson's disease patients with the freezing of gait symptom
Special section on affective and pervasive computing for healthcareIn this paper, we present a wearable assistant for Parkinson's disease (PD) patients with the freezing of gait (FOG) symptom. This wearable system uses on-body acceleration sensors to measure the patients' movements. It automatically detects FOG by ...
Comments