SVM Time Series Classification of Selected Gait Abnormalities

Rostovski, Jakob; Krivošei, Andrei; Kuusik, Alar; Ahmadov, Ulvi; Alam, Muhammad Mahtab

doi:10.1007/978-3-030-95593-9_16

Cited by 3 publications

(2 citation statements)

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“…The first algorithm, RTtsSVM-AD, is based on time-series SVM implementation from tslearn Python package, which uses Global Alignment Kernel (GAK) to make it possible for the SVM classifier to classify time-series data of different sizes (different lengths of data samples) [52]. In our previous work, it was tested as a classical full pattern offline classifier to classify the full normal and abnormal steps patterns in gait recordings and showed promising results for different gait types [53]. In our previous work [54], real-time instep anomaly detection RTtsSVM-AD algorithm was first evaluated, which collects the probability of abnormal class by classifying continuous streaming data using tslearn SVM as classification core.…”

Section: Real-time Tslearn Support Vector Machines Anomaly Detection ...mentioning

confidence: 99%

“…In our previous work, it was tested as a classical full pattern offline classifier to classify the full normal and abnormal steps patterns in gait recordings and showed promising results for different gait types [53]. In our previous work [54], real-time instep anomaly detection RTtsSVM-AD algorithm was first evaluated, which collects the probability of abnormal class by classifying continuous streaming data using tslearn SVM as classification core. It was tested using data for two subjects, and we showed as a concept that classical ML algorithms can be converted to real-time algorithms.…”

Section: Real-time Tslearn Support Vector Machines Anomaly Detection ...mentioning

confidence: 99%

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Novel Real-time In-step Gait Anomaly Detection Algorithms

Rostovski,

Krivošei,

Kuusik

et al. 2024

Preprint

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Goal: Real-time abnormality detection in gait analysis is an open challenge. The existing state-of-the-art is limited by the fact that it does not take into account ’in-step’ anomaly detection. Timely detection of gait abnormalities during the mid-swing phase of the step (i.e., within 50ms detection time) is required to make informed gait correction. To meet this requirement and overcome state of the art limitations, we present real-time anomaly detection algorithms, a new dataset, and a framework to estimate performance of anomaly detection algorithms. Methods: We propose real-time in-step anomaly detection algorithms namely, i) real-time tslearn support vector machines anomaly detection (RTtsSVM-AD), ii) real-time one class support vector machines anomaly detection (RTOCSVMAD), and iii) signal shape tracking anomaly detection (SSTAD). For comparative assessment, twenty-two healthy volunteers realistically simulated eight different characteristic deviations in human gait of certain lower extremity disabilities. Motion patterns were recorded using an inertial motion unit (IMU) placed on the forefoot. F1 score, recall, precision, real-time factor (RTF), as well as ”earliness” measures are estimated and analyzed. The ”earliness” is a new metric which defines the time between the beginning of a step and the moment in time when the step is classified as abnormal. Results: The achieved results demonstrate that the proposed algorithms can detect gait abnormalities in real-time during the mid-swing phase of an ongoing step. The average accuracy and F1 score achieved by the three algorithms are: 91% and 81% for SST-AD; 74% and 54.9% for RTOCSVM-AD; and 64.5% and 49.2% for RTtsSVMAD, respectively. The best average earliness is achieved by the SST-AD algorithm at 0.4 second from the initial-swing phase start. Conclusion: Based on the results, SST-AD is the best suited algorithm for real-time gait anomaly detection and should be considered to be used in future embedded assistive devices.

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Section: Real-time Tslearn Support Vector Machines Anomaly Detection ...mentioning

confidence: 99%

Section: Real-time Tslearn Support Vector Machines Anomaly Detection ...mentioning

confidence: 99%

Novel Real-time In-step Gait Anomaly Detection Algorithms

Rostovski,

Krivošei,

Kuusik

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Real-Time Gait Anomaly Detection Using 1D-CNN and LSTM

Rostovski,

Ahmadilivani,

Krivošei

et al. 2024

Communications in Computer and Information Science

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Anomaly detection and fall prevention represent one of the key research areas within gait analysis for patients suffering from neurological disorders. Deep Learning has penetrated into healthcare applications, encompassing disease diagnosis and anomaly prediction. Connected wearable medical sensors are emerging due to computationally expensive machine learning tasks, which traditionally require use of remote PC or cloud computing. However, to reduce needs for wireless communication channel throughput, for data processing latency, and increase service reliability and safety, on device machine learning is gaining attention. This paper presents an innovative approach that leverages one dimensional convolutional neural network (1D-CNN) and long-short term memory (LSTM) neural network for the real-time detection of abnormal gait patterns during the step. Real-time anomaly detection pertains to the algorithm’s ability to promptly detect true gait abnormality occurrence during the swing phase of an ongoing step.For the experiments, we have collected eight different common gait anomalies, simulated by 22 persons, using motion sensors containing multidimensional inertial measurement units (IMUs).Results have demonstrated that the proposed 1D-CNN-AD algorithm achieves an average accuracy of 95% and an average F1-score of 88% for all gait types and can run in true real-time. Average earliness for 1D-CNN-AD algorithm was 0.6 s, which is mid-swing phase of the step. Proposed LSTM-AD algorithm achieved average accuracy of 87% and average F1-score of 70% for all gait types.

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