Validation of a portable EMG device to assess muscle activity during free-living situations

Surface electromyography dataset from different movements of the hand using a portable and a non-portable device

This work presents the MuscleTracker Hand Movement dataset, containing Surface Electromyography (sEMG) data from the right arm of 49 healthy subjects without neuromuscular or cardiovascular issues. Subjects performed five hand movements-pronation with extended fingers, flexion, extension, pronation with flexed fingers, and relaxation-while standing, with one hand palm-down. Data was recorded from two sEMG channels using Biopac MP36 (1000 Hz) and MuscleTracker (512 Hz), with three and four repetitions per device, respectively, for each movement. The dataset includes 825 samples, along with subject details such as gender, age, physical condition, and, for MuscleTracker subjects, anthropometric measurements. This data supports machine-learning development for classifying hand gestures in sEMG signals, with applications in prosthetics control and human-computer interaction. In addition, validation experiments were performed to validate the database and stablish a comparison baseline.

Development of an Electromyography Signal Acquisition Prototype and Statistical Validation Against a Commercial Device

Electromyography (EMG) stands out as an accessible and inexpensive method for identifying muscle contractions on the surface and within deeper muscle tissues. Using specialized electronic circuits for amplification and filtering can help develop simple but effective systems for detecting and analyzing these signals. However, EMG devices developed by research teams frequently lack rigorous methodologies for validating the quality of the signals they record compared to those obtained by commercial systems that have undergone extensive testing and regulatory approval for market release. This underscores the critical need for standardized validation techniques to reliably assess the performance of experimental devices relative to established commercial equipment. Hence, this study introduces a methodology for the development and statistical validation of a laboratory EMG circuit compared with a professional device available on the market. The experiment simultaneously recorded the muscle electrical activity of 18 volunteers using two biosignal acquisition devices-a prototype EMG and a commercial system-both applied in parallel at the same recording site. Volunteers performed a series of finger and wrist extension movements to elicit myoelectric activity in these forearm muscles. To achieve this, it was necessary to develop not only the EMG signal conditioning board, but also two additional interface boards: one for enabling parallel recording on both devices and another for synchronizing the devices with the task programmatically controlled in Python that the volunteers were required to perform. The EMG signals generated during these tasks were recorded simultaneously by both devices. Subsequently, 22 feature indices commonly used for classifying muscular activity patterns were calculated from two-second temporal windows of the recordings to extract detailed temporal and spatial characteristics. Finally, the Mean Absolute Percentage Error (MAPE) was computed to compare the indices from the prototype with those from the commercial device, using this method as a validation system to assess the quality of the signals recorded by the prototype relative to the commercial equipment. A concordance of 87.6% was observed between the feature indices calculated from the recordings of both devices, suggesting high effectiveness and reliability of the EMG signals recorded by the prototype compared to the commercial device. These results validate the efficacy of our EMG prototype device and provide a solid foundation for the future evaluation of similar devices, ensuring their reliability, accuracy, and suitability for research or clinical applications.

Embedded Machine Learning System for Muscle Patterns Detection in a Patient with Shoulder Disarticulation

The integration of artificial intelligence (AI) models in the classification of electromyographic (EMG) signals represents a significant advancement in the design of control systems for prostheses. This study explores the development of a portable system that classifies the electrical activity of three shoulder muscles in real time for actuator control, marking a milestone in the autonomy of prosthetic devices. Utilizing low-power microcontrollers, the system ensures continuous EMG signal recording, enhancing user mobility. Focusing on a case study-a 42-year-old man with left shoulder disarticulation-EMG activity was recorded over two days using a specifically designed electronic board. Data processing was performed using the Edge Impulse platform, renowned for its effectiveness in implementing AI on edge devices. The first day was dedicated to a training session with 150 repetitions spread across 30 trials and three different movements. Based on these data, the second day tested the AI model's ability to classify EMG signals in new movement executions in real time. The results demonstrate the potential of portable AI-based systems for prosthetic control, offering accurate and swift EMG signal classification that enhances prosthetic user functionality and experience. This study not only underscores the feasibility of real-time EMG signal classification but also paves the way for future research on practical applications and improvements in the quality of life for prosthetic users.

Wearable Orofacial Technology and Orthodontics

Wearable technology to augment traditional approaches are increasingly being added to the arsenals of treatment providers. Wearable technology generally refers to electronic systems, devices, or sensors that are usually worn on or are in close proximity to the human body. Wearables may be stand-alone or integrated into materials that are worn on the body. What sets medical wearables apart from other systems is their ability to collect, store, and relay information regarding an individual's current body status to other devices operating on compatible networks in naturalistic settings. The last decade has witnessed a steady increase in the use of wearables specific to the orofacial region. Applications range from supplementing diagnosis, tracking treatment progress, monitoring patient compliance, and better understanding the jaw's functional and parafunctional activities. Orofacial wearable devices may be unimodal or incorporate multiple sensing modalities. The objective data collected continuously, in real time, in naturalistic settings using these orofacial wearables provide opportunities to formulate accurate and personalized treatment strategies. In the not-too-distant future, it is anticipated that information about an individual's current oral health status may provide patient-centric personalized care to prevent, diagnose, and treat oral diseases, with wearables playing a key role. In this review, we examine the progress achieved, summarize applications of orthodontic relevance and examine the future potential of orofacial wearables.

Surface respiratory electromyography and dyspnea in acute heart failure patients

Introduction and Objectives: Dyspnea is the most common symptom among hospitalized patients with heart failure (HF) but besides dyspnea questionnaires (which reflect the subjective patient sensation and are not fully validated in HF) there are no measurable physiological variables providing objective assessment of dyspnea in a setting of acute HF patients. Studies performed in respiratory patients suggest that the measurement of electromyographic (EMG) activity of the respiratory muscles with surface electrodes correlates well with dyspnea. Our aim was to test the hypothesis that respiratory muscles EMG activity is a potential marker of dyspnea severity in acute HF patients. Methods: Prospective and descriptive pilot study carried out in 25 adult patients admitted for acute HF. Measurements were carried out with a cardio-respiratory portable polygraph including EMG surface electrodes for measuring the activity of main (diaphragm) and accessory (scalene and pectoralis minor) respiratory muscles. Dyspnea sensation was assessed by means of the Likert 5 questionnaire. Data were recorded during 3 min of spontaneous breathing and after breathing at maximum effort for several cycles for normalizing data. An index to quantify the activity of each respiratory muscle was computed. This assessment was carried out within the first 24 h of admission, and at day 2 and 5. Results: Dyspnea score decreased along the three measured days. Diaphragm and scalene EMG index showed a positive and significant direct relationship with dyspnea score (p<0.001 and p = 0.003 respectively) whereas pectoralis minor muscle did not. Conclusion: In our pilot study, diaphragm and scalene EMG activity was associated with increasing severity of dyspnea. Surface respiratory EMG could be a useful objective tool to improve assessment of dyspnea in acute HF patients.

An automatic pre-processing method to detect and reject signal artifacts from full-shift field-work sEMG recordings of bilateral trapezius activity

Bipolar surface EMG (sEMG) signals of the trapezius muscles bilaterally were recorded continuously with a frequency of 800 Hz during full-shift field-work by a four-channel portable data logger. After recordings of 60 forest machine operators in Finland, Norway and Sweden, we discovered erroneous data. In short of any available procedure to handle these data, a method was developed to automatically discard erroneous data in the raw data reading files (Discarding Erroneous EPOchs (DESEPO) method. The DESEPO method automatically identifies, discards and adjusts the use of signal disturbances in order to achieve the best possible data use. An epoch is a 0.1 s period of raw sEMG signals and makes the basis for the RMS calculations. If erroneous signals constitute more than 30% of the epoch signals, this classifies for discharge of the present epoch. Non-valid epochs have been discarded, as well as all the subsequent epochs. The valid data for further analyses using the automatic detection resulted in an increase of acceptable data from an average of 2.15-6.5 h per day. The combination of long-term full-shift recordings and automatic data reduction procedures made it possible to use large amount of data otherwise discarded for further analyses.

Fuzzy Linguistic Summarization with Genetic Algorithm: An Application with Operational and Financial Healthcare Data

It is now well recognized that knowledge extracted from rich healthcare data play a vital role for delivery, management and planning of healthcare services. So far, however, there is not much study done on the domain of operational and financial healthcare data since, up to now, a great deal of works are dedicated to clinical/medical healthcare data for the purposes of diagnosis and treatment of diseases. In this paper, an attempt is made, by applying fuzzy linguistic summarization, for the first time to discover knowledge from operational and financial healthcare data. Fuzzy linguistic summarization, in its simplest term, provides natural language based summaries from a dataset in a human consistent way along with a degree of truth attached to each summary. While basically valuable, its benefit can be increased by only generating summaries with a degree of truth above than an indicated threshold value. A genetic algorithm is developed within this context in order to eliminate less promising and useless linguistic summaries. We assess the proposed approach experimentally on a real data and evaluate the generated summaries to gain actionable insights from them.

Hormonal and Neuromuscular Responses to Breastfeeding: A Pilot Study

OBJECTIVES

Difficult breastfeeding in the first weeks after birth may result in muscle tension in infants and activation of the maternal hypothalamic-pituitary-adrenal (HPA) axis and sympathetic nervous system (SNS). Our primary objective was to examine the feasibility of collecting neuroendocrine markers of maternal HPA axis and SNS activation (salivary cortisol and α-amylase [sAA]) and electromyographic (EMG) markers of infant distress during feeding in the first 2 weeks after birth. We also examined the relationships of these indices to each other and to mother-infant interactive behaviors during feeding.

METHODS

We recruited mothers in the postpartum unit of a teaching hospital and observed a feeding in the dyad's home. Cortisol and sAA were sampled before feeding, 10 min into feeding, at feeding end, and 20 min after feeding. Infant muscle activity was recorded continuously with an EMG data logger. We used the Nursing Child Assessment Feeding Scale to measure mother-infant interaction.

RESULTS

The 20 mothers reported no disruption to breastfeeding and no change in infant behavior due to collection measures. Mean cortisol levels decreased significantly; there was no significant change in sAA levels. Relationships were found between interactive behavior and trends in neuroendocrine biomarkers. Longer bursts of infant muscle activity were associated with higher levels of maternal cortisol during feeding but not mother-infant interactive behaviors.

CONCLUSIONS

Maternal salivary biomarkers and their association with feeding behaviors can be a useful tool for clinical longitudinal research beginning soon after birth. Infant EMG data may be useful for assessing maternal arousal.