Motion Intention Research Articles

Paying attention.

Intentional eye movements depend on where peripheral attention is voluntarily deployed beforehand, but they can be directed elsewhere shortly afterwards.

The Effect of Sensor Feature Inputs on Joint Angle Prediction across Simple Movements.

The use of wearable sensors, such as inertial measurement units (IMUs), and machine learning for human intent recognition in health-related areas has grown considerably. However, there is limited research exploring how IMU quantity and placement affect human movement intent prediction (HMIP) at the joint level. The objective of this study was to analyze various combinations of IMU input signals to maximize the machine learning prediction accuracy for multiple simple movements. We trained a Random Forest algorithm to predict future joint angles across these movements using various sensor features. We hypothesized that joint angle prediction accuracy would increase with the addition of IMUs attached to adjacent body segments and that non-adjacent IMUs would not increase the prediction accuracy. The results indicated that the addition of adjacent IMUs to current joint angle inputs did not significantly increase the prediction accuracy (RMSE of 1.92° vs. 3.32° at the ankle, 8.78° vs. 12.54° at the knee, and 5.48° vs. 9.67° at the hip). Additionally, including non-adjacent IMUs did not increase the prediction accuracy (RMSE of 5.35° vs. 5.55° at the ankle, 20.29° vs. 20.71° at the knee, and 14.86° vs. 13.55° at the hip). These results demonstrated how future joint angle prediction during simple movements did not improve with the addition of IMUs alongside current joint angle inputs.

Preparatory movement state enhances premovement EEG representations for brain–computer interfaces

Objective. Motor-related brain–computer interface (BCI) have a broad range of applications, with the detection of premovement intentions being a prominent use case. However, the electroencephalography (EEG) features during the premovement phase are not distinctly evident and are susceptible to attentional influences. These limitations impede the enhancement of performance in motor-based BCI. The objective of this study is to establish a premovement BCI encoding paradigm that integrates the preparatory movement state and validates its feasibility in improving the detection of movement intentions. Methods. Two button tasks were designed to induce subjects into a preparation state for two movement intentions (left and right) based on visual guidance, in contrast to spontaneous premovement. The low frequency movement-related cortical potentials (MRCPs) and high frequency event-related desynchronization (ERD) EEG data of 14 subjects were recorded. Extracted features were fused and classified using task related common spatial patterns (CSP) and CSP algorithms. Differences between prepared premovement and spontaneous premovement were compared in terms of time domain, frequency domain, and classification accuracy. Results. In the time domain, MRCPs features reveal that prepared premovement induce lower amplitude and earlier latency on both contralateral and ipsilateral motor cortex compared to spontaneous premovement, with susceptibility to the dominant hand’s influence. Frequency domain ERD features indicate that prepared premovement induce lower ERD values bilaterally, and the ERD recovery speed after button press is the fastest. By using the fusion approach, the classification accuracy increased from 78.92% for spontaneous premovement to 83.59% for prepared premovement (p < 0.05). Along with the 4.67% improvement in classification accuracy, the standard deviation decreased by 0.95. Significance. The research findings confirm that incorporating a preparatory state into premovement enhances neural representations related to movement. This encoding enhancement paradigm effectively improves the performance of motor-based BCI. Additionally, this concept has the potential to broaden the range of decodable movement intentions and related information in motor-related BCI.

First impressions from faces in dynamic approach-avoidance contexts.

Theoretical understanding of first impressions from faces has been closely associated with the proposal that rapid approach-avoidance decisions are needed during social interactions. Nevertheless, experimental work has rarely examined first impressions of people who are actually moving-instead extrapolating from photographic images. In six experiments, we describe the relationship between social attributions (dominance and trustworthiness) and the motion and apparent intent of a perceived person. We first show strong correspondence between judgments of photos and avatars of the same people (Experiment 1). Avatars were rated as more dominant and trustworthy when walking toward the viewer than when stationary (Experiment 2). Furthermore, avatars approaching the viewer were rated as more dominant than those avoiding (walking past) the viewer, or remaining stationary (Experiment 3). Trustworthiness was increased by movement, but not affected by approaching/avoiding paths. Surprisingly, dominance ratings increased both when avatars were approaching and being approached (Experiments 4-6), independently of agency. However, diverging movement (moving backward) reduced dominance ratings-again independently of agency (Experiment 6). These results demonstrate the close link between dominance judgments and approach and show the updatable nature of first impressions-their formation depended on the immediate dynamic context in a more subtle manner than previously suggested. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Development of Sensory Virtual Reality Interface Using EMG Signal-Based Grip Strength Reflection System

In virtual reality (VR), a factor that can maximize user immersion is the development of an intuitive and sensory interaction method. Physical devices such as controllers or data gloves of existing VR devices are used to control the movement intentions of the user, but their shortfall is that grip strength and detailed muscle strength cannot be reflected. Therefore, this study intended to establish a more sensory VR environment compared to existing methods by reflecting the grip strength of the flexor digitorum profundus of the user of the VR content. In this experiment, the muscle activity of the flexor digitorum profundus was obtained from six subjects based on surface electromyography, and four objects with differing intensity were created within a VR program in which the objects were made to be destroyed depending on muscle activity. As a result, satisfaction was improved because the users could sensitively interact with the objects inside the VR environment, and the intended motion control of the user was reflected in the VR content.

Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs.

Introduction: Surface electromyogram (sEMG) signals have been widely used in human upper limb force estimation and motion intention recognition. However, the electrocardiogram(ECG) artifact generated by the beating of the heart is a major factor that reduces the quality of the EMG signal when recording the sEMG signal from the muscle close to the heart. sEMG signals contaminated by ECG artifacts are difficult to be understood correctly. The objective of this paper is to effectively remove ECG artifacts from sEMG signals by a novel method. Methods: In this paper, sEMG and ECG signals of the biceps brachii, brachialis, and triceps muscle of the human upper limb will be collected respectively. Firstly, an improved multi-layer wavelet transform algorithm is used to preprocess the raw sEMG signal to remove the background noise and power frequency interference in the raw signal. Then, based on the theory of blind source separation analysis, an improved Fast-ICA algorithm was constructed to separate the denoising signals. Finally, an ECG discrimination algorithm was used to find and eliminate ECG signals in sEMG signals. This method consists of the following steps: 1) Acquisition of raw sEMG and ECG signals; 2) Decoupling the raw sEMG signal; 3) Fast-ICA-based signal component separation; 4) ECG artifact recognition and elimination. Results and discussion: The experimental results show that our method has a good effect on removing ECG artifacts from contaminated EMG signals. It can further improve the quality of EMG signals, which is of great significance for improving the accuracy of force estimation and motion intention recognition tasks. Compared with other state-of-the-art methods, our method can also provide the guiding significance for other biological signals.

Multi-mode adaptive control strategy for a lower limb rehabilitation robot.

Different patients have different rehabilitation requirements. It is essential to ensure the safety and comfort of patients at different recovery stages during rehabilitation training. This study proposes a multi-mode adaptive control method to achieve a safe and compliant rehabilitation training strategy. First, patients' motion intention and motor ability are evaluated based on the average human-robot interaction force per task cycle. Second, three kinds of rehabilitation training modes-robot-dominant, patient-dominant, and safety-stop-are established, and the adaptive controller can dexterously switch between the three training modes. In the robot-dominant mode, based on the motion errors, the patient's motor ability, and motion intention, the controller can adaptively adjust its assistance level and impedance parameters to help patients complete rehabilitation tasks and encourage them to actively participate. In the patient-dominant mode, the controller only adjusts the training speed. When the trajectory error is too large, the controller switches to the safety-stop mode to ensure patient safety. The stabilities of the adaptive controller under three training modes are then proven using Lyapunov theory. Finally, the effectiveness of the multi-mode adaptive controller is verified by simulation results.

How to use one surface electromyography sensor to recognize six hand movements for a mechanical hand in real time: a method based on Morse code.

Surface electromyography (sEMG) signal is a kind of physiological signal reflecting muscle activity and muscle force. At present, the existing methods of recognizing human motion intention need more than two sensors to recognize more than two kinds of movements, the sensor pasting positions are special, and the hardware conditions for execution are high. In this work, a real-time motion intention recognition method based on Morse code is proposed and applied to the mechanical hand. The short-time and long-term muscle contraction signals collected by a single sEMG sensor were extracted and encoded with the Morse code method, and then the developed mapping method from Morse code to six hand movements were used to recognize hand movements. The average recognition accuracy of hand movements was 94.8704 ± 2.3556%, the average adjusting time was 34.89s for all subjects, and the execution time of a single movement was 381ms. The corresponding experiment video can be found in the attachment to show the experiment. The method proposed in this work is a method with one sensor to recognize six movements, low hardware conditions, high recognition accuracy, and insensitive to the difference of sensor pasting position.

Fusion inception and transformer network for continuous estimation of finger kinematics from surface electromyography.

Decoding surface electromyography (sEMG) to recognize human movement intentions enables us to achieve stable, natural and consistent control in the field of human computer interaction (HCI). In this paper, we present a novel deep learning (DL) model, named fusion inception and transformer network (FIT), which effectively models both local and global information on sequence data by fully leveraging the capabilities of Inception and Transformer networks. In the publicly available Ninapro dataset, we selected surface EMG signals from six typical hand grasping maneuvers in 10 subjects for predicting the values of the 10 most important joint angles in the hand. Our model's performance, assessed through Pearson's correlation coefficient (PCC), root mean square error (RMSE), and R-squared (R2) metrics, was compared with temporal convolutional network (TCN), long short-term memory network (LSTM), and bidirectional encoder representation from transformers model (BERT). Additionally, we also calculate the training time and the inference time of the models. The results show that FIT is the most performant, with excellent estimation accuracy and low computational cost. Our model contributes to the development of HCI technology and has significant practical value.

A Sonomyography-Based Muscle Computer Interface for Individuals With Spinal Cord Injury.

Impairment of hand functions in individuals with spinal cord injury (SCI) severely disrupts activities of daily living. Recent advances have enabled rehabilitation assisted by robotic devices to augment the residual function of the muscles. Traditionally, electromyography-based muscle activity sensing interfaces have been utilized to sense volitional motor intent to drive robotic assistive devices. However, the dexterity and fidelity of control that can be achieved with electromyography-based control have been limited due to inherent limitations in signal quality. We have developed and tested a muscle-computer interface (MCI) utilizing sonomyography to provide control of a virtual cursor for individuals with motor-incomplete spinal cord injury. We demonstrate that individuals with SCI successfully gained control of a virtual cursor by utilizing contractions of muscles of the wrist joint. The sonomyography-based interface enabled control of the cursor at multiple graded levels demonstrating the ability to achieve accurate and stable endpoint control. Our sonomyography-based muscle-computer interface can enable dexterous control of upper-extremity assistive devices for individuals with motor-incomplete SCI.

Recognition of EEG-based movement intention combined with channel selection adopting deep learning methods

Brain-computer interface (BCI) is an emerging technology which provides a road to control communication and external devices. Electroencephalogram (EEG)-based motor imagery (MI) tasks recognition has important research significance for stroke, disability and others in BCI fields. However, enhancing the classification performance for decoding MI-related EEG signals presents a significant challenge, primarily due to the variability across different subjects and the presence of irrelevant channels. To address this issue, a novel hybrid structure is developed in this study to classify the MI tasks via deep separable convolution network (DSCNN) and bidirectional long short-term memory (BLSTM). First, the collected time-series EEG signals are initially processed into a matrix grid. Subsequently, data segments formed using a sliding window strategy are inputted into proposed DSCNN model for feature extraction (FE) across various dimensions. And, the spatial-temporal features extracted are then fed into the BLSTM network, which further refines vital time-series features to identify five distinct types of MI-related tasks. Ultimately, the evaluation results of our method demonstrate that the developed model achieves a 98.09% accuracy rate on the EEGMMIDB physiological datasets over a 4-second period for MI tasks by adopting full channels, outperforming other existing studies. Besides, the results of the five evaluation indexes of Recall, Precision, Test-auc, and F1-score also achieve 97.76%, 97.98%, 98.63% and 97.86%, respectively. Moreover, a Gradient-class Activation Mapping (GRAD-CAM) visualization technique is adopted to select the vital EEG channels and reduce the irrelevant information. As a result, we also obtained a satisfying outcome of 94.52% accuracy with 36 channels selected using the Grad-CAM approach. Our study not only provides an optimal trade-off between recognition rate and number of channels with half the number of channels reduced, but also it can also advances practical application research in the field of BCI rehabilitation medicine, effectively.

Lower Limb Motion Intent Recognition Based on Sensor Fusion and Fuzzy Multitask Learning

Lower Limb Motion Intent Recognition Based on Sensor Fusion and Fuzzy Multitask Learning

Supporting Migrant Children in Pediatric Settings: Lessons Learned from the US Migrant Humanitarian Crisis Response.

This article examines the influx of migrants to the United States and highlights current global and local immigration trends. The authors focus on migrant children-specifically the effect of migration trauma in the context of humanitarian responses to the intentional movement of migrants to Democrat-led cities across the US to humanize the compounded effects of migration trauma, restrictive immigration policies, and the current resettlement landscape for migrants. The authors are directly involved with supporting migrant arrivals who have relocated to Chicago from the southern border, and apply field knowledge to articulate current barriers to accessing health care and best practices within pediatric settings supporting migrant arrivals. Clinical and practice implications for medical providers in pediatric settings are included. The article also highlights the role of interdisciplinary collaboration in providing health care to asylum-seeking migrants and implications for transdisciplinary workforce development in this area. [Pediatr Ann. 2024;53(5):e171-e177.].

ANKLE JOINT MOTION RECOGNITION SYSTEM AND ALGORITHM OPTIMIZATION BASED ON PLANTAR PRESSURE

Due to the current focus of research on ankle rehabilitation robots on structural design, there is still limited research on ankle human–machine interaction technology. In order to enable rehabilitation robots to conduct personalized rehabilitation training based on patients’ ankle movement intentions, we propose a new ankle motion recognition method based on plantar pressure. First, we designed a plantar pressure collection system based on array sensors. Then, we collected nine types of ankle joint motion pressure data from five volunteers and conducted algorithm selection, data processing, and algorithm optimization. Finally, we proposed a small sample optimization algorithm based on support vector machine, with an average recognition rate of 93.16%. The recognition method proposed in this paper can be combined with ankle rehabilitation robots to achieve active rehabilitation functions, laying the foundation for the clinical application of active rehabilitation technology.

Theta activity discriminates high-level, species-specific body processes

Abstract Among social stimuli that trigger rapid reactions, body images occupy a prominent place. Given that bodies carry information about other agents’ intentions, actions and emotional expressions, a foundational question concerns the neural basis of body processing. Previous functional magnetic resonance imaging (fMRI) studies have investigated this but were not yet able to clarify the time course and its functional significance. The present EEG study investigated the role of slow oscillatory cortical activity in body processing and species-specificity. Human participants viewed naturalistic images of human and monkey bodies, faces, and objects, along with mosaic-scrambled versions to control for low-level visual features. Analysis of event-related theta power (4–7 Hz) combined with data-driven methods revealed a strong, body-evoked neural response that is specific to human bodies and spans a widespread scalp region during a time window of 200–550 ms after the onset of the body image. Our results corroborate recent research proposing a species-specific cortical network of human body processing. We submit that this network may play an essential role in linking body processes to movement intentions.

Design and Evaluation of CPU-, GPU-, and FPGA-Based Deployment of a CNN for Motor Imagery Classification in Brain-Computer Interfaces

Brain–computer interfaces (BCIs) have gained popularity in recent years. Among noninvasive BCIs, EEG-based systems stand out as the primary approach, utilizing the motor imagery (MI) paradigm to discern movement intentions. Initially, BCIs were predominantly focused on nonembedded systems. However, there is now a growing momentum towards shifting computation to the edge, offering advantages such as enhanced privacy, reduced transmission bandwidth, and real-time responsiveness. Despite this trend, achieving the desired target remains a work in progress. To illustrate the feasibility of this shift and quantify the potential benefits, this paper presents a comparison of deploying a CNN for MI classification across different computing platforms, namely, CPU-, embedded GPU-, and FPGA-based. For our case study, we utilized data from 29 participants included in a dataset acquired using an EEG cap for training the models. The FPGA solution emerged as the most efficient in terms of the power consumption–inference time product. Specifically, it delivers an impressive reduction of up to 89% in power consumption compared to the CPU and 71% compared to the GPU and up to a 98% reduction in memory footprint for model inference, albeit at the cost of a 39% increase in inference time compared to the GPU. Both the embedded GPU and FPGA outperform the CPU in terms of inference time.

Feasibility and Preliminary Impact of a Web-Based Mind Body Intervention for Older Dementia Caregivers.

Mind-body interventions focused on intentional breathing and movement have been found to mitigate the negative effects of caregiving such as such as stress, psychosocial distress, and emotional distress associated for persons living with Alzheimer's disease and related dementias. The objective was to assess the feasibility and acceptability and preliminary impacts of our 12-week mind body intervention "Gentle Yoga and Yogic Breathing" for caregivers of persons living with dementia on health outcomes including mutuality, depression and anxiety, loneliness and social support, quality of life, and physical function. We conducted a single-group cohort study in which 20 caregivers were enrolled. Data were collected at baseline and at the 12-week post-intervention endpoint. The intervention was acceptable; 75% (n = 15/20) completed the study; 16 completed post-study questionnaires. Very few experienced technical issues; 31% (n = 3) most commonly reported as poor internet connectivity, 75% (n = 12/16) perceived a health benefit, 88% (n = 14/16) perceived improved day-to-day mood, and 100% (n = 16/16) would recommend the intervention to other caregivers. Although there was minimal change from baseline to 12 weeks, for health outcomes, there were very small improvements noted in anxiety and overall health. There were no reported adverse events. The intervention was well received and is feasible and acceptable for future studies of stress and health management interventions for caregivers of persons living with dementia.Registered with https://www.ClinicalTrials.gov (NCT03853148).

A systematic investigation of detectors for low signal-to-noise ratio EMG signals

Background Active participation of stroke survivors during robot-assisted movement therapy is essential for sensorimotor recovery. Robot-assisted therapy contingent on movement intention is an effective way to encourage patients’ active engagement. For severely impaired stroke patients with no residual movements, a surface electromyogram (EMG) has been shown to be a viable option for detecting movement intention. Although numerous algorithms for EMG detection exist, the detector with the highest accuracy and lowest latency for low signal-to-noise ratio (SNR) remains unknown. Methods This study, therefore, investigates the performance of 13 existing EMG detection algorithms on simulated low SNR (0dB and -3dB) EMG signals generated using three different EMG signal models: Gaussian, Laplacian, and biophysical model. The detector performance was quantified using the false positive rate (FPR), false negative rate (FNR), and detection latency. Any detector that consistently showed FPR and FNR of no more than 20%, and latency of no more than 50ms, was considered an appropriate detector for use in robot-assisted therapy. Results The results indicate that the Modified Hodges detector – a simplified version of the threshold-based Hodges detector introduced in the current study – was the most consistent detector across the different signal models and SNRs. It consistently performed for ~90% and ~40% of the tested trials for 0dB and -3dB SNR, respectively. The two statistical detectors (Gaussian and Laplacian Approximate Generalized Likelihood Ratio) and the Fuzzy Entropy detectors have a slightly lower performance than Modified Hodges. Conclusions Overall, the Modified Hodges, Gaussian and Laplacian Approximate Generalized Likelihood Ratio, and the Fuzzy Entropy detectors were identified as the potential candidates that warrant further investigation with real surface EMG data since they had consistent detection performance on low SNR EMG data.

CYCLOPS: A cyclists’ orientation data acquisition system using RGB camera and inertial measurement units (IMU)

This paper introduces CYCLOPS, an acquisition system developed to capture images and inertial measurement data of moving cyclists from a vehicle. The development of CYCLOPS addresses the need to acquire useful data for training machine learning models capable of predicting the motion intentions of cyclists on urban roads. Considering its application, it is a completely original development. The system consists of two devices. The first device is installed on the bicycle and is based on an electronic acquisition board comprising an inertial measurement unit (IMU), a microcontroller, and a transceiver for sending the cyclist’s acceleration and orientation data to a vehicle. The second device is installed on the vehicle and uses the same board architecture to acquire the vehicle’s accelerations and orientations, along with an RGB monocular camera. The data is stored in real-time in a laptop’s drive for subsequent analysis and manipulation. The hardware architecture is presented in detail, including the designs to install the devices, for IMUs configuration, and software installation on the laptop. All design and software files required to develop the proposed system are available for download at: doi.org/10.17632/3yx5y8b7tm.1, licensed under the Open-source license CC BY 4.0.

Gestalt Therapy Research: Developing a Phenomenological, Aesthetic, Field-Oriented Approach

ABSTRACT The author proposes a frame of reference for Gestalt therapists who want to do research from a field perspective. She outlines fundamental aspects of phenomenological research that includes aesthetics of contact and the relational therapeutic approach. Ethical aspects of Gestalt therapy research, from the need of outcome research to the responsibility to show what we do in our practice, are described, in support of the discovery of new territories to advance our humanity. Two examples are offered: research on the construct of aesthetic relational knowing (ARK) to explore therapeutic intuition and responsiveness from a Gestalt viewpoint, and research to describe the process of change in the dyad therapist/client, seen as a “dance” of reciprocal, intentional movements that draws on attachment learnings.