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@article{195446,
        author = {Krati Goyal and Sanskar Dewangan and Adrika Shukla and Dr. A. Umamageswari},
        title = {Freezing of Gait Detection in Parkinson’s Disease Using Multi-modal Features and Support Vector Machine},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {15244-15261},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195446},
        abstract = {Freezing of gait (FOG) is a highly disabling symptom in patients with Parkinson’s disease. FOG occurs as a sudden inability or great difficulty in initiating or maintaining walking. The accurate and timely detection of FOG episodes plays a critical role in improving the monitoring and rehabilitation processes for patients with Parkinson’s disease. Moreover, it also plays an important role in improving the quality of life for patients with Parkinson’s disease. However, the detection of FOG episodes is considered to be a challenging task due to the complex physiological and neural processes involved in the detection process. This article proposes a novel framework for detecting freezing of gait episodes in patients with Parkinson’s disease. The proposed framework employs a combination of multiple data streams, which include an electroencephalogram (EEG) signal, an electromyogram (EMG) signal, an electrocardiogram (ECG) signal, and a skin conductance (SC) signal. This combination of signals is collected from patients suffering from Parkinson’s disease. The collected signals are processed, and features are extracted to detect FOG episodes using a Support Vector Machine (SVM) classifier. The experimental results reveal that the proposed framework, which employs a combination of multiple sensors, has a higher accuracy in detecting FOG episodes in patients suffering from Parkinson’s disease compared to a single sensor-based system. This proposed framework proves the efficiency of machine learning techniques in detecting freezing of gait in patients suffering from Parkinson’s disease.},
        keywords = {Freezing of Gait, Parkinson’s Disease, Multimodal Sensors, Machine Learning, Support Vector Machine, Biomedical Signal Processing},
        month = {April},
        }