Novelty Detection using Deep Normative Modeling for IMU-Based Abnormal Movement Monitoring in Parkinson’s Disease and Autism Spectrum Disorders
Open accessAbstract
Detecting and monitoring of abnormal movement behaviors in patients with Parkinson’s Disease (PD) and individuals with Autism Spectrum Disorders (ASD) are beneficial for adjusting care and medical treatment in order to improve the patient’s quality of life. Supervised methods commonly used in the literature need annotation of data, which is a time-consuming and costly process. In this paper, we propose deep normative modeling as a probabilistic novelty detection method, in which we model the distribution of normal human movements recorded by wearable sensors and try to detect abnormal movements in patients with PD and ASD in a novelty detection framework. In the proposed deep normative model, a movement disorder behavior is treated as an extreme of the normal range or, equivalently, as a deviation from the normal movements. Our experiments on three benchmark datasets indicate the effectiveness of the proposed method, which outperforms one-class SVM and the reconstruction-based novelty detection approaches. Our contribution opens the door toward modeling normal human movements during daily activities using wearable sensors and eventually real-time abnormal movement detection in neuro-developmental and neuro-degenerative disorders.
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Several works have introduced systems and methods for monitoring and assessing Parkinson disease symptoms.
“Specifically, several works have introduced systems and methods for monitoring and assessing Parkinson disease (PD) symptoms [31,32,33,34,35,36].”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [34]
Autoencoders are also commonly used to detect abnormal muscle movements, such as Parkinson's Disease and Autism Spectrum Disorder (ASD).
“Autoencoders are also commonly used to detect abnormal muscle movements, such as Parkinson’s Disease and Autism Spectrum Disorder (ASD).”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [34]
Novelty detection is a reliable method for FOG detection and abnormal gait detection with a low computational burden and without the need for labeled data.
“Despite these results, the use of novelty detection presents a reliable method for FOG detection and abnormal gait detection with a low computational burden, without the need for labeled data [66].”
From Deep Learning Approaches for Detecting Freezing of Gait in Parkinson’s Disease Patients through On-Body Acceleration Sensors (2020) · cites this paper as [66]
Novelty detection is a feasible method for fast detection of abnormal gait patterns in related applications.
“Despite these results, the use of novelty detection presents a feasible method for the fast detection of abnormal gait patterns in related applications [66].”
From Deep Learning Approaches for Detecting Freezing of Gait in Parkinson’s Disease Patients through On-Body Acceleration Sensors (2020) · cites this paper as [66]
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Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687 · see citing works
An Introduction to Statistical Modeling of Extreme Values
Coles, S. (2001) · Springer Series in Statistics · doi:10.1007/978-1-4471-3675-0 · Cited by 7,352 · see citing works
The relationship between Precision-Recall and ROC curves
Davis, J., & Goadrich, M. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143874 · Cited by 6,559 · see citing works
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y., & Ghahramani, Z. (2015) · arXiv · doi:10.48550/arxiv.1506.02142 · Cited by 6,471 · see citing works
Estimating the Support of a High-Dimensional Distribution
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001) · Neural Computation · doi:10.1162/089976601750264965 · Cited by 6,168 · see citing works
Diagnostic Criteria for Parkinson Disease
Gelb, D. J., Oliver, E., & Gilman, S. (1999) · Archives of Neurology · doi:10.1001/archneur.56.1.33 · Cited by 2,822 · see citing works
Real-World Anomaly Detection in Surveillance Videos
Sultani, W., Chen, C., & Shah, M. A. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00678 · Cited by 2,248 · see citing works
Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014) · Signal Processing · doi:10.1016/j.sigpro.2013.12.026 · Cited by 1,649 · see citing works
Gait Analysis Using Wearable Sensors
Tao, W., Liu, T., Zheng, R., & Feng, H. (2012) · Sensors · doi:10.3390/s120202255 · Cited by 1,170 · see citing works
High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning
Erfani, S. M., Rajasegarar, S., Karunasekera, S., & Leckie, C. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.03.028 · Cited by 1,162 · see citing works
Lam, K. S. L., & Aman, M. G. (2007) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-006-0213-z · Cited by 793 · see citing works
Understanding heterogeneity in clinical cohorts using normative models: Beyond case-control studies
Marquand, A. F., Rezek, I., Buitelaar, J., & Beckmann, C. F. (2016) · Biological Psychiatry · doi:10.1016/j.biopsych.2015.12.023 · Cited by 683 · see citing works
Lord, C., Cook, E. H., Leventhal, B. L., & Amaral, D. G. (2000) · Neuron · doi:10.1016/s0896-6273(00)00115-x · Cited by 555 · see citing works
Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes
Sabokrou, M., Fayyaz, M., Fathy, M., Moayed, Z., & Klette, R. (2018) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2018.02.006 · Cited by 471 · see citing works
A Review of Anomaly based Intrusion Detection Systems
Jyothsna, V., Prasad, V. V. R., & Prasad, K. M. (2011) · International Journal of Computer Applications · doi:10.5120/3399-4730 · Cited by 387 · see citing works
Novelty detection for the identification of masses in mammograms
Tarassenko, L. (1995) · doi:10.1049/cp:19950597 · Cited by 351 · see citing works
Marquand, A. F., Wolfers, T., Mennes, M., Buitelaar, J. K., & Beckmann, C. F. (2016) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2016.04.002 · Cited by 254 · see citing works
Rodríguez-Martín, D., Samà, A., Pérez-López, C., Català, A., Moreno Arostegui, J. M., Cabestany, J., Bayés, À., Alcaine, S., Mestre, B., Prats, A., et al. (2017) · PLOS ONE · doi:10.1371/journal.pone.0171764 · Cited by 237 · see citing works
One-class classifier networks for target recognition applications
Moya, M. M., Koch, M. W., & Hostetler, L. D. (1993) · NASA STI/Recon Technical Report N · Cited by 233 · see citing works
Online Detection of Freezing of Gait with Smartphones and Machine Learning Techniques
Mazilu, S., Hardegger, M., Zhu, Z., Roggen, D., Troester, G., Plotnik, M., & Hausdorff, J. (2012) · Proceedings of the 6th International Conference on Pervasive Computing Technologies for Healthcare · doi:10.4108/icst.pervasivehealth.2012.248680 · Cited by 231 · see citing works
Camps, J., Samà, A., Martín, M., Rodríguez-Martín, D., Pérez-López, C., Moreno Arostegui, J. M., Cabestany, J., Català, A., Alcaine, S., Mestre, B., et al. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.10.017 · Cited by 224 · see citing works
Computationally Efficient Convolved Multiple Output Gaussian Processes
Álvarez, M. A., & Lawrence, N. D. (2009) · Apollo (University of Cambridge) · doi:10.5555/1953048.2021048 · Cited by 218 · see citing works
Automatic detection of freezing of gait events in patients with Parkinson's disease
Tripoliti, E. E., Tzallas, A. T., Tsipouras, M. G., Rigas, G., Bougia, P., Leontiou, M., Konitsiotis, S., Chondrogiorgi, M., Tsouli, S. G., & Fotiadis, D. I. (2012) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2012.10.016 · Cited by 196 · see citing works
Davison, A. C., & Huser, R. (2015) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-010814-020133 · Cited by 186 · see citing works
An Unsupervised Approach for Automatic Activity Recognition Based on Hidden Markov Model Regression
Trabelsi, D., Mohammed, S., Chamroukhi, F., Oukhellou, L., & Amirat, Y. (2013) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2013.2256349 · Cited by 167 · see citing works
Rajasegarar, S., Leckie, C. A., Bezdek, J. C., et al. (2010) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2010.2051543 · Cited by 159 · see citing works
ADF: An Anomaly Detection Framework for Large-Scale PM2.5 Sensing Systems
Chen, L., Ho, Y.-H., Hsieh, H.-H., Huang, S.-T., Lee, H.-C., & Mahajan, S. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2766085 · Cited by 131 · see citing works
A Survey on Anomaly Based Host Intrusion Detection System
Jose, S., Malathi, D., Reddy, B., et al. (2018) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1000/1/012049 · Cited by 121 · see citing works
An anomaly detection system based on variable N-gram features and one-class SVM
Khreich, W., Khosravifar, B., Hamou‐Lhadj, A., & Talhi, C. (2017) · Information and Software Technology · doi:10.1016/j.infsof.2017.07.009 · Cited by 115 · see citing works
Morgan, L., Wetherby, A. M., & Barber, A. (2008) · Journal of Child Psychology and Psychiatry · doi:10.1111/j.1469-7610.2008.01904.x · Cited by 107 · see citing works
Uitti, R. J., Baba, Y., Wszołek, Z. K., & Putzke, D. J. (2005) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2004.10.007 · Cited by 102 · see citing works
Extending the Generalised Pareto Distribution for Novelty Detection in High-Dimensional Spaces
Clifton, D. A., Clifton, L., Hugueny, S., & Tarassenko, L. (2013) · Journal of Signal Processing Systems · doi:10.1007/s11265-013-0835-2 · Cited by 100 · see citing works
Bächlin, M., Roggen, D., Tröster, G., Plotnik, M., Inbar, N., Meidan, I., Herman, T., Brozgol, M., Shaviv, E., Giladi, N., et al. (2009) · International Symposium on Wearable Computers · doi:10.1109/iswc.2009.14 · Cited by 100 · see citing works
A novel anomaly detection algorithm for sensor data under uncertainty
Islam, R. U., Hossain, M. S., & Andersson, K. (2016) · Soft Computing · doi:10.1007/s00500-016-2425-2 · Cited by 87 · see citing works
Ziegler, G., Ridgway, G. R., Dahnke, R., & Christian Gaser, B. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.04.018 · Cited by 86 · see citing works
Skeleton-Based Abnormal Gait Detection
Nguyen, T.-N., Huynh, H.-H., & Meunier, J. (2016) · Sensors · doi:10.3390/s16111792 · Cited by 83 · see citing works
Extreme value statistics for noveltydetectionin biomedical data processing
Roberts, S. (2000) · IEE Proceedings - Science Measurement and Technology · doi:10.1049/ip-smt:20000841 · Cited by 75 · see citing works
Detecting unseen falls from wearable devices using channel-wise ensemble of autoencoders
Khan, S. S., & Taati, B. (2016) · Expert systems with applications · doi:10.1016/j.eswa.2017.06.011 · Cited by 74 · see citing works
An On-Node Processing Approach for Anomaly Detection in Gait
Cola, G., Avvenuti, M., Vecchio, A., et al. (2015) · IEEE Sensors Journal · doi:10.1109/jsen.2015.2464774 · Cited by 73 · see citing works
Applying Deep Learning to Stereotypical Motor Movement Detection in Autism Spectrum Disorders
Rad, N. M., & Furlanello, C. (2016) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2016.0178 · Cited by 69 · see citing works
Self-citationThe role of wrist-mounted inertial sensors in detecting gait freeze episodes in Parkinson’s disease
Mazilu, S., Blanke, U., Calatroni, A., Gazit, E., Hausdorff, J. M., & Tröster, G. (2016) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2015.12.007 · Cited by 62 · see citing works
Großekathöfer, U., Manyakov, N. V., Mihajlović, V., Pandina, G., Skalkin, A., Ness, S. L., Bangerter, A., & Goodwin, M. S. (2017) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2017.00009 · Cited by 58 · see citing works
Goodwin, M. S., Haghighi, M., Tang, Q., Akçakaya, M., Deniz Erdogmus, B., & Intille, S. S. (2014) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2632048.2632096 · Cited by 57 · see citing works
It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals
Rakitsch, B., Lippert, C., Borgwardt, K., et al. (2013) · Max Planck Digital Library · Cited by 54 · see citing works
Novelty detection: Unlabeled data definitely help
Scott, C. D., & Blanchard, G. (2009) · Cited by 45 · see citing works
Known Unknowns: Novelty Detection in Condition Monitoring
Quinn, J. A., & Williams, C. K. I. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-72847-4_1 · Cited by 33 · see citing works
Stereotypical Motor Movement Detection in Dynamic Feature Space
Rad, N. M., Kia, S. M., Zarbo, C., Jurman, G., Venuti, P., & Furlanello, C. (2016) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2016.0076 · Cited by 29 · see citing works
Self-citationAn unsupervised framework for detecting anomalous messages from syslog log files
Vaarandi, R., Blumbergs, B., & Kont, M. (2018) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms.2018.8406283 · Cited by 26 · see citing works
Detection of Emerging Faults on Industrial Gas Turbines Using Extended Gaussian Mixture Models
Zhang, Y., Bingham, C. M., Martínez-García, M., et al. (2017) · International Journal of Rotating Machinery · doi:10.1155/2017/5435794 · Cited by 20 · see citing works
Normative Modeling of Neuroimaging Data Using Scalable Multi-task Gaussian Processes
Kia, S. M., & Marquand, A. F. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00931-1_15 · Cited by 20 · see citing works
Combined Support Vector Novelty Detection for Multi-channel Combustion Data
Clifton, L., Yin, H., Clifton, D. A., & Zhang, Y. (2007) · IEEE International Conference on Networking, Sensing and Control (ICNSC) · doi:10.1109/icnsc.2007.372828 · Cited by 12 · see citing works
Gaussian mixture model for time series-based structural damage detection
Słoński, M. (2020) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.24423/cames.83 · Cited by 9 · see citing works
Rad, N. M., Kia, S. M., Zarbo, C., van Laarhoven, T., Jurman, G., Venuti, P., Marchiori, E., & Furlanello, C. (2017) · Signal Processing · doi:10.1016/j.sigpro.2017.10.011 · Cited by 6 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 10
- [8]Hammerla N.Y. Halloran S. Ploetz T. Deep, convolutional, and recurrent models for human activity recognition using wearables arXiv 2016 1604.08880
- [21]Kia S.M. Backmann C.F. Marquand A.F. Scalable Multi-Task Gaussian Process Tensor Regression for Normative Modeling of Structured Variation in Neuroimaging Data arXiv 2018 1808.00036
- [35]Rad N.M. Bizzego A. Kia S.M. Jurman G. Venuti P. Furlanello C. Convolutional neural network for stereotypical motor movement detection in autism arXiv 2015 1511.01865
- [41]Vasilev A. Golkov V. Lipp I. Sgarlata E. Tomassini V. Jones D.K. Cremers D. q-Space Novelty Detection with Variational Autoencoders arXiv 2018 1806.02997
- [42]Chen X. Pawlowski N. Rajchl M. Glocker B. Konukoglu E. Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging arXiv 2018 1806.05452
- [55]Albinali F. Goodwin M.S. Intille S.S. Recognizing stereotypical motor movements in the laboratory and classroom: A case study with children on the autism spectrum Proceedings of the 11th International Conference on Ubiquitous Computing Orlando, FL, USA 30 September–3 October 2009 71 80
- [57]Bonilla E.V. Chai K.M. Williams C. Multi-task Gaussian process prediction Proceedings of the Neural Information Processing Systems 2008 Vancouver, BC, Canada 8–10 December 2008 153 160
- [62]Kingma D.P. Welling M. Auto-encoding variational bayes arXiv 2013 1312.6114
- [63]Makhzani A. Shlens J. Jaitly N. Goodfellow I. Frey B. Adversarial autoencoders arXiv 2015 1511.05644
- [64]Goodfellow I. Pouget-Abadie J. Mirza M. Xu B. Warde-Farley D. Ozair S. Courville A. Bengio Y. Generative adversarial nets Proceedings of the Neural Information Processing Systems 2014 Montreal, QC, Canada 8–13 December 2014 2672 2680
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Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
Zhang, S., Li, Y., Zhang, S., Shahabi, F., Xia, S., Deng, Y., & Alshurafa, N. (2022) · Sensors · doi:10.3390/s22041476 · Cited by 625 · see citing works
Sigcha, L., Costa, N., Pavón, I., Costa, S., Arezes, P., López, J. M., & De Arcas, G. (2020) · Sensors · doi:10.3390/s20071895 · Cited by 126 · see citing works
IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review
Bo, F., Yerebakan, M., Dai, Y., Wang, W., Li, J., Hu, B., & Gao, S. (2022) · Healthcare · doi:10.3390/healthcare10071210 · Cited by 35 · see citing works
- Authors
- Mohammadian Rad, N., Van Laarhoven, T., Furlanello, C., & Marchiori, E.
- Affiliations
- Institute for Computing and Information Science, Radboud University, 6525EC Nijmegen, The Netherlands
- Fondazione Bruno Kessler (IT)
- Radboud University Nijmegen (NL)
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2018-10-19
- Volume
- 18
- Issue
- 10
- Pages
- 3533
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC6211024
- OpenAlex
- W2897762618
- Cited by
- 76
- References
- 54
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 13 · 2023: 10 · 2022: 16 · 2021: 10 · 2020: 13 · 2019: 5 · 2018: 1
- FWCI
- 4.03
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Radboud Repository (Radboud University) (submittedVersion)
- DSpace (Open University in the Netherlands) (submittedVersion, cc-by)
- DSpace (Open University in the Netherlands) (submittedVersion, other-oa)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (612.001.352)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Keywords
- novelty detection, abnormal movement detection, Parkinson's disease, autism spectrum disorder, inertial measurement unit, wearable sensors, probabilistic anomaly detection, one-class support vector machine, real-time monitoring
- MeSH terms
- Autism Spectrum Disorder, Activities of Daily Living, Female, Humans, Male, Movement, Parkinson Disease, Quality of Life, Dyskinesias
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