Leveraging deep feature learning for wearable sensors based handwritten character recognition

Singh, Shashank; Chaturvedi, Amrita

doi:10.1016/j.bspc.2022.104198

Cited by 17 publications

(10 citation statements)

References 51 publications

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“…These methodologies encompass a wide range of applications, including the tracking of 3D finger joint angles, [64] classification of arm movement patterns, [67] identification of wrist movements, [150] and handwritten character recognition. [151] The development of an artificial throat based on facial EMG signal represents a novel and pioneering application within the domain of HMI. This approach offers a potential solution to patients with limited vocalization abilities.…”

Section: Emg With Machine Learning Techniquesmentioning

confidence: 99%

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Recent Developments and Future Directions of Wearable Skin Biosignal Sensors

Kim,

Min,

2023

Advanced Sensor Research

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This review article explores the transformative advancements in wearable biosignal sensors powered by machine learning, focusing on four notable biosignals: electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG), and photoplethysmogram (PPG). The integration of machine learning with these biosignals has led to remarkable breakthroughs in various medical monitoring and human–machine interface applications. For ECG, machine learning enables automated heartbeat classification and accurate disease detection, improving cardiac healthcare with early diagnosis and personalized interventions. EMG technology, combined with machine learning, facilitates real‐time prediction and classification of human motions, revolutionizing applications in sports medicine, rehabilitation, prosthetics, and virtual reality interfaces. EEG analysis powered by machine learning goes beyond traditional clinical applications, enabling brain activity understanding in psychology, neurology, and human–computer interaction, and holds promise in brain–computer interfaces. PPG, augmented with machine learning, has shown exceptional progress in diagnosing and monitoring cardiovascular and respiratory disorders, offering non‐invasive and accurate healthcare solutions. These integrated technologies, powered by machine learning, open new avenues for medical monitoring and human–machine interaction, shaping the future of healthcare.

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Section: Emg With Machine Learning Techniquesmentioning

confidence: 99%

Section: Machine Learning‐assisted Wearable Biosensorsmentioning

confidence: 99%

Recent Developments and Future Directions of Wearable Skin Biosignal Sensors

Kim,

Min,

2023

Advanced Sensor Research

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“…Another study used a combination of tsfresh and an SVM to perform classification of human tasks based on inertial information captured by smart glasses [23]. Outside of activity recognition the library has been used for the task of handwriting recognition using wearable sensors, achieving 98% classification accuracy for the 26 characters in the Latin alphabet [24].…”

Section: Related Workmentioning

confidence: 99%

Time-Series-Based Feature Selection and Clustering for Equine Activity Recognition Using Accelerometers

et al. 2023

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With over 16 million horses worldwide and nearly 60,000 sport horses registered to the International Federation for Equestrian Sports database, tracking the activities and performance of these equines is becoming an important aspect in horse management. To perform this activity recognition, Inertial Measurement Units (IMUs) are often used in combination with machine learning algorithms. These often require large labeled datasets to be trained. To this end, a data-efficient algorithm is proposed that requires only 3 minutes of labeled calibration data. This is achieved by combining supervised feature selection, using the tsfresh time-series feature calculation library and the Kendall rank correlation coefficient, with a distance-based clustering algorithm. The generalizability performance of the algorithm is tested by evaluating on a dataset captured with leg-mounted IMUs and on a dataset captured using a neck-mounted IMU. On both datasets, the algorithm achieved accuracies of 95%, comparable to state-of-the-art deep learning approaches, when calibrating and evaluating using the same horse. When the algorithm was calibrated on data from multiple horses and evaluated on horses that were not in the calibration dataset, a 15% drop in classification accuracy is observed. The proposed algorithm is compared with fully supervised algorithms like convolutional neutral network, support vector machine, and random forest in terms of accuracy achieved with respect to the size of the labeled data using calibration. Our approach achieved accuracies that were similar to these classical algorithms whilst only using 10-15% the amount of labeled data.

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“…Owing to advantages such as good generalization performance on real-life data and discriminative power, SVM has been highly successful in pattern recognition [2]. handwritten character [3], and other practical applications [4]. However, a critical barrier that hinders the broader usage of SVM is its long training time caused by demanding more computation time and memory when handling large scale data and complex problems in the age of big data [4].…”

Section: Introductionmentioning

confidence: 99%

PSVM-MR: A Parallel Support Vector Machine Algorithm Based on MapReduce

Mao,

Neelakandan

et al. 2024

Preprint

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In big data environments,utilizing a parallel Support Vector Machine (SVM) can significantly expedite the training process. However, prior efforts have faced challenges due to the excessive deviation of subset distribution, inadequate performance of parallel training, and poor filtering ability of non-support vector. To tackle these obstacles, this paper proposes a novel approach called SVM algorithm based on MapReduce (PSVM-MR). Firstly, a data partition method based on relative entropy (DP-RE) calculates relative entropy to prevent excessive deviation of subset distribution introduced. Secondly, it presents a redundancy level-removing method based on cosine similarity (RLR-CS) that addresses the inadequate performance of parallel training by eliminating the redundancy levels in the cascade structure. Finally, a non-support vector filtering method (NSVF) that enhances the ability of non-support vector filtering by combining rough identification and singular vector identification is proposed. The proposed algorithm demonstrates higher parallel efficiency and lower training costs compared to the general parallel SVM algorithm.

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Leveraging deep feature learning for wearable sensors based handwritten character recognition

Cited by 17 publications

References 51 publications

Recent Developments and Future Directions of Wearable Skin Biosignal Sensors

Recent Developments and Future Directions of Wearable Skin Biosignal Sensors

Time-Series-Based Feature Selection and Clustering for Equine Activity Recognition Using Accelerometers

PSVM-MR: A Parallel Support Vector Machine Algorithm Based on MapReduce

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