A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human–Machine Interactivities and Biomedical Applications

Prediction of Dexterous Finger Forces With Forearm Rotation Using Motoneuron Discharges

Motor unit (MU) discharge information obtained via electromyogram (EMG) decomposition can be used to decode dexterous multi-finger movement intention for neural-machine interfaces (NMI). However, the variation of the motor unit action potential (MUAP) shape resulted from forearm rotation leads to the decreased performance of EMG decomposition, especially under the real-time condition and then the degradation of motion decoding accuracy. The object of this study was to develop a method to realize the accurate extraction of MU discharge information across forearm pronated/supinated positions in the real-time condition for dexterous multi-finger force prediction. The FastICA-based EMG decomposition technique was used and the proposed method obtained multiple separation vectors for each MU at different forearm positions in the initialization phase. Under the real-time condition, the MU discharge information was extracted adaptively using the separation vector extracted at the nearest forearm position. As comparison, the previous method that utilized a single constant separation vector to extract MU discharges across forearm positions and the conventional method that utilized the EMG amplitude information were also performed. The results showed that the proposed method obtained a significantly better performance compared with the other two methods, manifested in a larger coefficient of determination ( [Formula: see text] and a smaller root mean squared error (RMSE) between the predicted and recorded force. Our results demonstrated the feasibility and the effectiveness of the proposed method to extract MU discharge information during forearm rotation for dexterous force prediction under the real-time conditions. Further development of the proposed method could potentially promote the application of the EMG decomposition technique for continuous dexterous motion decoding in a realistic NMI application scenario.

Layered Fusion Reconstruction Based on Fuzzy Features for Multi-Conductivity Electrical Impedance Tomography

In medical imaging, detecting tissue anomalies is vital for accurate diagnosis and effective treatment. Electrical impedance tomography (EIT) is a non-invasive technique that monitors the changes in electrical conductivity within tissues in real time. However, the current challenge lies in simply and accurately reconstructing multi-conductivity distributions. This paper introduces a layered fusion framework for EIT to enhance imaging in multi-conductivity scenarios. The method begins with pre-imaging and extracts the main object from the fuzzy image to form one layer. Then, the voltage difference in the other layer, where the local anomaly is located, is estimated. Finally, the corresponding conductivity distribution is established, and multiple layers are fused to reconstruct the multi-conductivity distribution. The simulation and experimental results demonstrate that compared to traditional methods, the proposed method significantly improves multi-conductivity separation, precise anomaly localization, and robustness without adding uncertain parameters. Notably, the proposed method has demonstrated exceptional accuracy in local anomaly detection, with positional errors as low as 1% and size errors as low as 33%, which significantly outperforms the traditional method with respective minimum errors of 9% and 228%. This method ensures a balance between the simplicity and accuracy of the algorithm. At the same time, it breaks the constraints of traditional linear methods, struggling to identify multi-conductivity distributions, thereby providing new perspectives for clinical EIT.

Recent advances of additively manufactured noninvasive kinematic biosensors

The necessity of reliable measurement data assessment in the realm of human life has experienced exponential growth due to its extensive utilization in health monitoring, rehabilitation, surgery, and long-term treatment. As a result, the significance of kinematic biosensors has substantially increased across various domains, including wearable devices, human-machine interaction, and bioengineering. Traditionally, the fabrication of skin-mounted biosensors involved complex and costly processes such as lithography and deposition, which required extensive preparation. However, the advent of additive manufacturing has revolutionized biosensor production by facilitating customized manufacturing, expedited processes, and streamlined fabrication. AM technology enables the development of highly sensitive biosensors capable of measuring a wide range of kinematic signals while maintaining a low-cost aspect. This paper provides a comprehensive overview of state-of-the-art noninvasive kinematic biosensors created using diverse AM technologies. The detailed development process and the specifics of different types of kinematic biosensors are also discussed. Unlike previous review articles that primarily focused on the applications of additively manufactured sensors based on their sensing data, this article adopts a unique approach by categorizing and describing their applications according to their sensing frequencies. Although AM technology has opened new possibilities for biosensor fabrication, the field still faces several challenges that need to be addressed. Consequently, this paper also outlines these challenges and provides an overview of future applications in the field. This review article offers researchers in academia and industry a comprehensive overview of the innovative opportunities presented by kinematic biosensors fabricated through additive manufacturing technologies.

Insole Systems for Disease Diagnosis and Rehabilitation: A Review

Some chronic diseases, including Parkinson's disease (PD), diabetic foot, flat foot, stroke, elderly falling, and knee osteoarthritis (KOA), are related to orthopedic organs, nerves, and muscles. The interaction of these three parts will generate a comprehensive result: gait. Furthermore, the lesions in these regions can produce abnormal gait features. Therefore, monitoring the gait features can assist medical professionals in the diagnosis and analysis of these diseases. Nowadays, various insole systems based on different sensing techniques have been developed to monitor gait and aid in medical research. Hence, a detailed review of insole systems and their applications in disease management can greatly benefit researchers working in the field of medical engineering. This essay is composed of the following sections: the essay firstly provides an overview of the sensing mechanisms and parameters of typical insole systems based on different sensing techniques. Then this essay respectively discusses the three stages of gait parameters pre-processing, respectively: pressure reconstruction, feature extraction, and data normalization. Then, the relationship between gait features and pathogenic mechanisms is discussed, along with the introduction of insole systems that aid in medical research; Finally, the current challenges and future trends in the development of insole systems are discussed.

Feasibility of force myography for the direct control of an assistive robotic hand orthosis in non-impaired individuals

BACKGROUND

Assistive robotic hand orthoses can support people with sensorimotor hand impairment in many activities of daily living and therefore help to regain independence. However, in order for the users to fully benefit from the functionalities of such devices, a safe and reliable way to detect their movement intention for device control is crucial. Gesture recognition based on force myography measuring volumetric changes in the muscles during contraction has been previously shown to be a viable and easy to implement strategy to control hand prostheses. Whether this approach could be efficiently applied to intuitively control an assistive robotic hand orthosis remains to be investigated.

METHODS

In this work, we assessed the feasibility of using force myography measured from the forearm to control a robotic hand orthosis worn on the hand ipsilateral to the measurement site. In ten neurologically-intact participants wearing a robotic hand orthosis, we collected data for four gestures trained in nine arm configurations, i.e., seven static positions and two dynamic movements, corresponding to typical activities of daily living conditions. In an offline analysis, we determined classification accuracies for two binary classifiers (one for opening and one for closing) and further assessed the impact of individual training arm configurations on the overall performance.

RESULTS

We achieved an overall classification accuracy of 92.9% (averaged over two binary classifiers, individual accuracies 95.5% and 90.3%, respectively) but found a large variation in performance between participants, ranging from 75.4 up to 100%. Averaged inference times per sample were measured below 0.15 ms. Further, we found that the number of training arm configurations could be reduced from nine to six without notably decreasing classification performance.

CONCLUSION

The results of this work support the general feasibility of using force myography as an intuitive intention detection strategy for a robotic hand orthosis. Further, the findings also generated valuable insights into challenges and potential ways to overcome them in view of applying such technologies for assisting people with sensorimotor hand impairment during activities of daily living.

Wearable Electromyography Classification of Epileptic Seizures: A Feasibility Study

Accurate diagnosis and classification of epileptic seizures can greatly support patient treatments. As many epileptic seizures are convulsive and have a motor component, the analysis of muscle activity can provide valuable information for seizure classification. Therefore, this paper present a feasibility study conducted on healthy volunteers, focusing on tracking epileptic seizures movements using surface electromyography signals (sEMG) measured on human limb muscles. For the experimental studies, first, compact wireless sensor nodes were developed for real-time measurement of sEMG on the gastrocnemius, flexor carpi ulnaris, biceps brachii, and quadriceps muscles on the right side and the left side. For the classification of the seizure, a machine learning model has been elaborated. The 16 common sEMG time-domain features were first extracted and examined with respect to discrimination and redundancy. This allowed the features to be classified into irrelevant features, important features, and redundant features. Redundant features were examined with the Big-O notation method and with the average execution time method to select the feature that leads to lower complexity and reduced processing time. The finally selected six features were explored using different machine learning classifiers to compare the resulting classification accuracy. The results show that the artificial neural network (ANN) model with the six features: IEMG, WAMP, MYOP, SE, SKEW, and WL, had the highest classification accuracy (99.95%). A further study confirms that all the chosen eight sensors are necessary to reach this high classification accuracy.

Multi-Sensing Techniques with Ultrasound for Musculoskeletal Assessment: A Review

The study of muscle contractions generated by the muscle-tendon unit (MTU) plays a critical role in medical diagnoses, monitoring, rehabilitation, and functional assessments, including the potential for movement prediction modeling used for prosthetic control. Over the last decade, the use of combined traditional techniques to quantify information about the muscle condition that is correlated to neuromuscular electrical activation and the generation of muscle force and vibration has grown. The purpose of this review is to guide the reader to relevant works in different applications of ultrasound imaging in combination with other techniques for the characterization of biological signals. Several research groups have been using multi-sensing systems to carry out specific studies in the health area. We can divide these studies into two categories: human-machine interface (HMI), in which sensors are used to capture critical information to control computerized prostheses and/or robotic actuators, and physiological study, where sensors are used to investigate a hypothesis and/or a clinical diagnosis. In addition, the relevance, challenges, and expectations for future work are discussed.