Speed controller-based fuzzy logic for a biosignal-feedbacked cycloergometer

Ensastiga, S. Aldo Lechuga; Rodríguez‐Reséndiz, Juvenal; Estévez-Bén, Adyr A.

doi:10.1080/10255842.2021.1977799

Cited by 5 publications

(7 citation statements)

References 30 publications

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“…Researchers have proposed a solution called the cyclic force meter, which implements a treatment machine's fuzzy speed controller. Through various experimental tests, the effectiveness of the designed controller has been verified, greatly improving the system's robustness (Ensastiga et al, 2022 ). Additionally, the biomedical engineering task of classifying motion-corresponding EMG signals has received extensive attention.…”

Section: Related Workmentioning

confidence: 99%

Application of Entity-BERT model based on neuroscience and brain-like cognition in electronic medical record entity recognition

Lu,

Jiang,

Shi

et al. 2023

Front. Neurosci.

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IntroductionIn the medical field, electronic medical records contain a large amount of textual information, and the unstructured nature of this information makes data extraction and analysis challenging. Therefore, automatic extraction of entity information from electronic medical records has become a significant issue in the healthcare domain.MethodsTo address this problem, this paper proposes a deep learning-based entity information extraction model called Entity-BERT. The model aims to leverage the powerful feature extraction capabilities of deep learning and the pre-training language representation learning of BERT(Bidirectional Encoder Representations from Transformers), enabling it to automatically learn and recognize various entity types in medical electronic records, including medical terminologies, disease names, drug information, and more, providing more effective support for medical research and clinical practices. The Entity-BERT model utilizes a multi-layer neural network and cross-attention mechanism to process and fuse information at different levels and types, resembling the hierarchical and distributed processing of the human brain. Additionally, the model employs pre-trained language and sequence models to process and learn textual data, sharing similarities with the language processing and semantic understanding of the human brain. Furthermore, the Entity-BERT model can capture contextual information and long-term dependencies, combining the cross-attention mechanism to handle the complex and diverse language expressions in electronic medical records, resembling the information processing method of the human brain in many aspects. Additionally, exploring how to utilize competitive learning, adaptive regulation, and synaptic plasticity to optimize the model's prediction results, automatically adjust its parameters, and achieve adaptive learning and dynamic adjustments from the perspective of neuroscience and brain-like cognition is of interest.Results and discussionExperimental results demonstrate that the Entity-BERT model achieves outstanding performance in entity recognition tasks within electronic medical records, surpassing other existing entity recognition models. This research not only provides more efficient and accurate natural language processing technology for the medical and health field but also introduces new ideas and directions for the design and optimization of deep learning models.

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Section: Related Workmentioning

confidence: 99%

Application of Entity-BERT model based on neuroscience and brain-like cognition in electronic medical record entity recognition

Lu,

Jiang,

Shi

et al. 2023

Front. Neurosci.

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“…Multi-head attention contains several modules called heads that have their own queries and a set of key value pairs expressed by fully connected layers from the original queries and a set of key value pairs fed to the input layer, see Equation (2). The advantage of using multiple heads lies in the ability to combine the different contexts found from each of the heads into one complex output.…”

Section: Transformer Modelmentioning

confidence: 99%

“…Essentially, it aims at identifying human behavior based on data from sensors, available from personal devices such as smartphones, tablets, or smartwatches that can collect data from a wide sample of users and classify the signals using machine learning methods [1]. The technology of detecting human activities using mobile devices has great potential in medicine where it is possible to monitor patients with various diagnoses [2][3][4][5] and control compliance with treatment procedures or to use it as prevention against performing prohibited activities [6][7][8][9]. In addition to health monitoring and rehabilitation, this technology can be used in gaming [10], human-robot interaction and robotics [11,12], and sports [13].…”

Section: Introductionmentioning

confidence: 99%

Wearable Sensor-Based Human Activity Recognition with Transformer Model

Ľuptáková

Kubovčík

Pospı́chal

2022

Sensors

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Computing devices that can recognize various human activities or movements can be used to assist people in healthcare, sports, or human–robot interaction. Readily available data for this purpose can be obtained from the accelerometer and the gyroscope built into everyday smartphones. Effective classification of real-time activity data is, therefore, actively pursued using various machine learning methods. In this study, the transformer model, a deep learning neural network model developed primarily for the natural language processing and vision tasks, was adapted for a time-series analysis of motion signals. The self-attention mechanism inherent in the transformer, which expresses individual dependencies between signal values within a time series, can match the performance of state-of-the-art convolutional neural networks with long short-term memory. The performance of the proposed adapted transformer method was tested on the largest available public dataset of smartphone motion sensor data covering a wide range of activities, and obtained an average identification accuracy of 99.2% as compared with 89.67% achieved on the same data by a conventional machine learning method. The results suggest the expected future relevance of the transformer model for human activity recognition.

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“…Non-contact heart rate monitoring can be performed in many ways [ 1 , 2 , 3 ]. However, two techniques are the most widespread, i.e., the imaging and capacitive ECG methods.…”

Section: Introductionmentioning

confidence: 99%

Non-Contact Monitoring of ECG in the Home Environment—Selecting Optimal Electrode Configuration

Bujnowski

Osinski

Przystup

et al. 2022

Sensors

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Capacitive electrocardiography (cECG) is most often used in wearable or embedded measurement systems. The latter is considered in the paper. An optimal electrocardiographic lead, as an individual feature, was determined based on model studies. It was defined as the possibly highest value of the R-wave amplitude measured on the back of the examined person. The lead configuration was also analyzed in terms of minimizing its susceptibility to creating motion artifacts. It was found that the direction of the optimal lead coincides with the electrical axis of the heart. Moreover, the electrodes should be placed in the areas preserving the greatest voltage and at the same time characterized by the lowest gradient of the potential. Experimental studies were conducted using the developed measurement system on a group of 14 people. The ratio of the R-wave amplitude (as measured on the back and chest, using optimal leads) was less than 1 while the SNR reached at least 20 dB. These parameters allowed for high-quality QRS complex detection with a PPV of 97%. For the “worst” configurations of the leads, the signals measured were practically uninterpretable.

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Speed controller-based fuzzy logic for a biosignal-feedbacked cycloergometer

Cited by 5 publications

References 30 publications

Application of Entity-BERT model based on neuroscience and brain-like cognition in electronic medical record entity recognition

Application of Entity-BERT model based on neuroscience and brain-like cognition in electronic medical record entity recognition

Wearable Sensor-Based Human Activity Recognition with Transformer Model

Non-Contact Monitoring of ECG in the Home Environment—Selecting Optimal Electrode Configuration

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