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INTRODUCTION: Several studies have assessed and validated the impact of exoskeletons on back and shoulder muscle activation; however, limited research has explored the role that exoskeletons could play in mitigating lower arm-related disorders. This study assessed the impact of Ironhand, an active hand exoskeleton (H-EXO) designed to reduce grip force exertion, on worker exertion levels using a two-phase experimental design. METHOD: Ten male participants performed a controlled, simulated drilling activity, while three male participants completed an uncontrolled concrete demolition activity. The impact of the exoskeleton was assessed in terms of muscle activity across three different muscles using electromyography (EMG), perceived exertion, and perceived effectiveness. RESULTS: Results indicate that peak muscle activation decreased across the target muscle group when the H-EXO was used, with the greatest reduction (27%) observed in the Extensor Carpi Radialis (ECR). Using the exoskeleton in controlled conditions did not significantly influence perceived exertion levels. Users indicated that the H-EXO was a valuable technology and expressed willingness to use it for future tasks. PRACTICAL APPLICATIONS: This study showcases how glove-based exoskeletons can potentially reduce wrist-related disorders, thereby improving safety and productivity among workers. Future work should assess the impact of the H-EXO in various tasks, different work environments and configurations, and among diverse user groups.
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Electromiografía , Dispositivo Exoesqueleto , Mano , Humanos , Masculino , Proyectos Piloto , Adulto , Mano/fisiología , Fuerza de la Mano/fisiología , Músculo Esquelético/fisiología , Adulto Joven , Esfuerzo Físico/fisiología , Análisis y Desempeño de Tareas , Industria de la Construcción/instrumentaciónRESUMEN
Stroke is an acute cerebrovascular disease in which sudden interruption of blood supply to the brain or rupture of cerebral blood vessels cause damage to brain cells and consequently impair the patient's motor and cognitive abilities. A novel rehabilitation training model integrating brain-computer interface (BCI) and virtual reality (VR) not only promotes the functional activation of brain networks, but also provides immersive and interesting contextual feedback for patients. In this paper, we designed a hand rehabilitation training system integrating multi-sensory stimulation feedback, BCI and VR, which guides patients' motor imaginations through the tasks of the virtual scene, acquires patients' motor intentions, and then carries out human-computer interactions under the virtual scene. At the same time, haptic feedback is incorporated to further increase the patients' proprioceptive sensations, so as to realize the hand function rehabilitation training based on the multi-sensory stimulation feedback of vision, hearing, and haptic senses. In this study, we compared and analyzed the differences in power spectral density of different frequency bands within the EEG signal data before and after the incorporation of haptic feedback, and found that the motor brain area was significantly activated after the incorporation of haptic feedback, and the power spectral density of the motor brain area was significantly increased in the high gamma frequency band. The results of this study indicate that the rehabilitation training of patients with the VR-BCI hand function enhancement rehabilitation system incorporating multi-sensory stimulation can accelerate the two-way facilitation of sensory and motor conduction pathways, thus accelerating the rehabilitation process.
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Interfaces Cerebro-Computador , Electroencefalografía , Mano , Rehabilitación de Accidente Cerebrovascular , Realidad Virtual , Humanos , Mano/fisiología , Rehabilitación de Accidente Cerebrovascular/métodos , Rehabilitación de Accidente Cerebrovascular/instrumentación , Retroalimentación Sensorial , Interfaz Usuario-Computador , Corteza Motora/fisiologíaRESUMEN
BACKGROUND: Hand function assessment heavily relies on specific task scenarios, making it challenging to ensure validity and reliability. In addition, the wide range of assessment tools, limited and expensive data recording, and analysis systems further aggravate the issue. However, smartphones provide a promising opportunity to address these challenges. Thus, the built-in, high-efficiency sensors in smartphones can be used as effective tools for hand function assessment. OBJECTIVE: This review aims to evaluate existing studies on hand function evaluation using smartphones. METHODS: An information specialist searched 8 databases on June 8, 2023. The search criteria included two major concepts: (1) smartphone or mobile phone or mHealth and (2) hand function or function assessment. Searches were limited to human studies in the English language and excluded conference proceedings and trial register records. Two reviewers independently screened all studies, with a third reviewer involved in resolving discrepancies. The included studies were rated according to the Mixed Methods Appraisal Tool. One reviewer extracted data on publication, demographics, hand function types, sensors used for hand function assessment, and statistical or machine learning (ML) methods. Accuracy was checked by another reviewer. The data were synthesized and tabulated based on each of the research questions. RESULTS: In total, 46 studies were included. Overall, 11 types of hand dysfunction-related problems were identified, such as Parkinson disease, wrist injury, stroke, and hand injury, and 6 types of hand dysfunctions were found, namely an abnormal range of motion, tremors, bradykinesia, the decline of fine motor skills, hypokinesia, and nonspecific dysfunction related to hand arthritis. Among all built-in smartphone sensors, the accelerometer was the most used, followed by the smartphone camera. Most studies used statistical methods for data processing, whereas ML algorithms were applied for disease detection, disease severity evaluation, disease prediction, and feature aggregation. CONCLUSIONS: This systematic review highlights the potential of smartphone-based hand function assessment. The review suggests that a smartphone is a promising tool for hand function evaluation. ML is a conducive method to classify levels of hand dysfunction. Future research could (1) explore a gold standard for smartphone-based hand function assessment and (2) take advantage of smartphones' multiple built-in sensors to assess hand function comprehensively, focus on developing ML methods for processing collected smartphone data, and focus on real-time assessment during rehabilitation training. The limitations of the research are 2-fold. First, the nascent nature of smartphone-based hand function assessment led to limited relevant literature, affecting the evidence's completeness and comprehensiveness. This can hinder supporting viewpoints and drawing conclusions. Second, literature quality varies due to the exploratory nature of the topic, with potential inconsistencies and a lack of high-quality reference studies and meta-analyses.
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Mano , Teléfono Inteligente , Humanos , Mano/fisiopatología , Mano/fisiología , Masculino , Femenino , Enfermedad de Parkinson/fisiopatología , Telemedicina/instrumentación , AncianoRESUMEN
Intercepting moving targets is a fundamental skill in human behavior, influencing various domains such as sports, gaming, and other activities. In these contexts, precise visual processing and motor control are crucial for adapting and navigating effectively. Nevertheless, there are still some gaps in our understanding of how these elements interact while intercepting a moving target. This study explored the dynamic interplay among eye movements, pupil size, and interceptive hand movements, with visual and motion uncertainty factors. We developed a simple visuomotor task in which participants used a joystick to interact with a computer-controlled dot that moved along two-dimensional trajectories. This virtual system provided the flexibility to manipulate the target's speed and directional uncertainty during chase trials. We then conducted a geometric analysis based on optimal angles for each behavior, enabling us to distinguish between simple tracking and predictive trajectories that anticipate future positions of the moving target. Our results revealed the adoption of a strong interception strategy as participants approached the target. Notably, the onset and amount of optimal interception strategy depended on task parameters, such as the target's speed and frequency of directional changes. Furthermore, eye-tracking data showed that participants continually adjusted their gaze speed and position, continuously adapting to the target's movements. Finally, in successful trials, pupillary responses predicted the amount of optimal interception strategy while exhibiting an inverse relationship in trials without collisions. These findings reveal key interactions among visuomotor parameters that are crucial for solving complex interception tasks.
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Movimientos Oculares , Desempeño Psicomotor , Humanos , Masculino , Femenino , Desempeño Psicomotor/fisiología , Adulto , Movimientos Oculares/fisiología , Adulto Joven , Pupila/fisiología , Percepción de Movimiento/fisiología , Tecnología de Seguimiento Ocular , Mano/fisiología , Movimiento/fisiologíaRESUMEN
BACKGROUND: Previous studies have reported that the sense of "self" is associated with specific brain regions and neural network activities. In addition, the mirror system, which functions when executing or observing an action, might contribute to differentiating the self from others and form the basis of the sense of self as a fundamental physical representation. This study investigated whether differences in mu suppression, an indicator of mirror system activity, reflect cognitions related to self-other discrimination. METHODS: The participants were 30 of healthy college students. The participants observed short video clips of hand movements performed by themselves or actors from two perspectives (i.e., first-person and third-person). The electroencephalogram (EEG) mu rhythm (8-13 Hz) was measured during video observation as an index of mirror neuron system activity. EEG activity related to self-detection was analyzed using participants' hand movements as self-relevant stimuli. RESULTS: The results showed that mu suppression in the 8-13-Hz range exhibited perspective-dependent responses to self/other stimuli. There was a significant self-oriented mu suppression response in the first-person perspective. However, the study found no significant response orientation in the third-person perspective. The results suggest that mirror system activity may involve self-other discrimination differently depending on the perspective. CONCLUSIONS: In summary, this study examined the mirror system's activity for self and others using the EEG's mu suppression. As a result, it was suggested that differences in self and others or perspectives may influence mu suppression.
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Electroencefalografía , Mano , Movimiento , Humanos , Femenino , Masculino , Mano/fisiología , Adulto Joven , Movimiento/fisiología , Adulto , Neuronas Espejo/fisiología , Ondas Encefálicas/fisiologíaRESUMEN
This single-participant case study examines the feasibility of using custom virtual reality (VR) gaming software in the home environment for low-dose Hand Arm Bimanual Intensive Training (HABIT). A 10-year-old with right unilateral cerebral palsy participated in this trial. Fine and gross motor skills as well as personal goals for motor outcomes were assessed before and after the intervention using the Box and Blocks Test, Nine-Hole Peg Test, and Canadian Occupational Performance Measure. Movement intensities collected via the VR hardware accelerometers, VR game scores, and task accuracy were recorded via the HABIT-VR software as indices of motor performance. The child and family were instructed to use the HABIT-VR games twice daily for 30 minutes over a 14-day period and asked to record when they used the system. The child used the system and completed the 14-hour, low-dose HABIT-VR intervention across 22 days. There was no change in Box and Blocks Test and Nine-Hole Peg Test scores before and after the intervention. Canadian Occupational Performance Measure scores increased but did not reach the clinically relevant threshold, due to high scores at baseline. Changes in motor task intensities during the use of VR and mastery of the VR bimanual tasks suggested improved motor efficiency. This case study provides preliminary evidence that HABIT-VR is useful for promoting adherence to HABIT activities and for the maintenance of upper extremity motor skills in the home setting.
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Parálisis Cerebral , Estudios de Factibilidad , Realidad Virtual , Humanos , Parálisis Cerebral/rehabilitación , Parálisis Cerebral/fisiopatología , Niño , Masculino , Destreza Motora/fisiología , Juegos de Video , Brazo , Mano/fisiología , FemeninoRESUMEN
This study addresses the need for highly sensitive tools to evaluate hand strength, particularly grasp and pinch strength, which are vital for diagnosing and rehabilitating conditions affecting hand function. Current devices like the Jamar dynamometer and Martin Vigorimeter, although reliable, fail to measure extremely low force or pressure values required for individuals with severe hand impairments. This research introduces a novel device, a modified Martin Vigorimeter, utilizing an ultra-soft latex chamber and differential pressure measurement to detect minute pressure changes, thus significantly enhancing sensitivity. The device offers a cost-effective solution, making advanced hand strength evaluation more accessible for clinical and research applications. Future research should validate its accuracy across diverse populations and settings, exploring its broader implications for hand rehabilitation and occupational health.
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Fuerza de la Mano , Presión , Fuerza de la Mano/fisiología , Humanos , Mano/fisiología , Fuerza de Pellizco/fisiología , Dinamómetro de Fuerza Muscular , Diseño de EquipoRESUMEN
Class demonstrations where the lecturer's hand motor actions are observable generally have a positive effect on the learning of motor skills because they trigger an effortless process of embodied simulations. Whether the learning of cognitive skills is likewise affected by such visualisations is yet to be investigated and might depend on the learning content as well as other factors. This study aimed to investigate whether showing the lecturer's hand via a document camera during an introductory financial accounting class affects student learning (transfer performance), cognitive load responses, and note-taking behaviour compared to a writing pad where the lecturer's hand is not visible. The study utilised a quasi-experimental design in an in-person setting, with a pre-test and post-test comparison of two groups of participants: one group that viewed a lecture video without the lecturer's hand being visible (n = 509), and another group that viewed the same lecture with the lecturer's hand being visible (n = 571). The results showed that the with-hand group had a significantly higher increase in test scores compared to the without-hand group. However, the visibility of the hand did not significantly impact cognitive load or note-taking behaviour. The findings have important practical implications for education, as incorporating non-verbal cues such as the lecturer's hand may effectively enhance learning cognitive skills.
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Cognición , Mano , Aprendizaje , Humanos , Masculino , Femenino , Aprendizaje/fisiología , Mano/fisiología , Cognición/fisiología , Adulto , Destreza Motora/fisiología , Adulto Joven , Estudiantes/psicologíaRESUMEN
The loss of a hand disrupts the sophisticated neural pathways between the brain and the hand, severely affecting the level of independence of the patient and the ability to carry out daily work and social activities. Recent years have witnessed a rapid evolution of surgical techniques and technologies aimed at restoring dexterous motor functions akin to those of the human hand through bionic solutions, mainly relying on probing of electrical signals from the residual nerves and muscles. Here, we report the clinical implementation of an interface aimed at achieving this goal by exploiting muscle deformation, sensed through passive magnetic implants: the myokinetic interface. One participant with a transradial amputation received an implantation of six permanent magnets in three muscles of the residual limb. A truly self-contained myokinetic prosthetic arm embedding all hardware components and the battery within the prosthetic socket was developed. By retrieving muscle deformation caused by voluntary contraction through magnet localization, we were able to control in real time a dexterous robotic hand following both a direct control strategy and a pattern recognition approach. In just 6 weeks, the participant successfully completed a series of functional tests, achieving scores similar to those achieved when using myoelectric controllers, a standard-of-care solution, with comparable physical and mental workloads. This experience raised conceptual and technical limits of the interface, which nevertheless pave the way for further investigations in a partially unexplored field. This study also demonstrates a viable possibility for intuitively interfacing humans with robotic technologies.
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Amputados , Miembros Artificiales , Fuerza de la Mano , Imanes , Diseño de Prótesis , Robótica , Humanos , Amputados/rehabilitación , Fuerza de la Mano/fisiología , Robótica/instrumentación , Masculino , Músculo Esquelético/fisiología , Extremidad Superior , Mano/fisiología , Adulto , Electromiografía , Muñones de Amputación/fisiopatología , Contracción Muscular/fisiología , Implantación de PrótesisRESUMEN
Hand Movement Recognition (HMR) with sEMG is crucial for artificial hand prostheses. HMR performance mostly depends on the feature information that is fed to the classifiers. However, sEMG often captures noise like power line interference (PLI) and motion artifacts. This may extract redundant and insignificant feature information, which can degrade HMR performance and increase computational complexity. This study aims to address these issues by proposing a novel procedure for automatically removing PLI and motion artifacts from experimental sEMG signals. This will make it possible to extract better features from the signal and improve the categorization of various hand movements. Empirical mode decomposition and energy entropy thresholding are utilized to select relevant mode components for artifact removal. Time domain features are then used to train classifiers (kNN, LDA, SVM) for hand movement categorization, achieving average accuracies of 92.36%, 93.63%, and 98.12%, respectively, across subjects. Additionally, muscle contraction efforts are classified into low, medium, and high categories using this technique. Validation is performed on data from ten subjects performing eight hand movement classes and three muscle contraction efforts with three surface electrode channels. Results indicate that the proposed preprocessing improves average accuracy by 9.55% with the SVM classifier, significantly reducing computational time.
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Algoritmos , Artefactos , Electromiografía , Mano , Movimiento , Reconocimiento de Normas Patrones Automatizadas , Procesamiento de Señales Asistido por Computador , Humanos , Electromiografía/métodos , Mano/fisiología , Reconocimiento de Normas Patrones Automatizadas/métodos , Masculino , Contracción Muscular , Adulto , Miembros Artificiales , Femenino , Movimiento (Física) , Músculo Esquelético/fisiologíaRESUMEN
The hybrid brain-computer interface (BCI) is verified to reduce disadvantages of conventional BCI systems. Transcranial electrical stimulation (tES) can also improve the performance and applicability of BCI. However, enhancement in BCI performance attained solely from the perspective of users or solely from the angle of BCI system design is limited. In this study, a hybrid BCI system combining MI and SSVEP was proposed. Furthermore, transcranial alternating current stimulation (tACS) was utilized to enhance the performance of the proposed hybrid BCI system. The stimulation interface presented a depiction of grabbing a ball with both of hands, with left-hand and right-hand flickering at frequencies of 34 Hz and 35 Hz. Subjects watched the interface and imagined grabbing a ball with either left hand or right hand to perform SSVEP and MI task. The MI and SSVEP signals were processed separately using filter bank common spatial patterns (FBCSP) and filter bank canonical correlation analysis (FBCCA) algorithms, respectively. A fusion method was proposed to fuse the features extracted from MI and SSVEP. Twenty healthy subjects took part in the online experiment and underwent tACS sequentially. The fusion accuracy post-tACS reached 90.25% ± 11.40%, which was significantly different from pre-tACS. The fusion accuracy also surpassed MI accuracy and SSVEP accuracy respectively. These results indicated the superior performance of the hybrid BCI system and tACS would improve the performance of the hybrid BCI system.
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Algoritmos , Interfaces Cerebro-Computador , Electroencefalografía , Imaginación , Estimulación Transcraneal de Corriente Directa , Humanos , Masculino , Imaginación/fisiología , Femenino , Estimulación Transcraneal de Corriente Directa/métodos , Adulto , Adulto Joven , Voluntarios Sanos , Desempeño Psicomotor/fisiología , Mano/fisiología , Reproducibilidad de los Resultados , Potenciales Evocados Visuales/fisiología , Potenciales Evocados Motores/fisiologíaRESUMEN
Long-term electroencephalography (EEG) recordings have primarily been used to study resting-state fluctuations. These recordings provide valuable insights into various phenomena such as sleep stages, cognitive processes, and neurological disorders. However, this study explores a new angle, focusing for the first time on the evolving nature of EEG dynamics over time within the context of movement. Twenty-two healthy individuals were measured six times from 2 p.m. to 12 a.m. with intervals of 2 h while performing four right-hand gestures. Analysis of movement-related cortical potentials (MRCPs) revealed a reduction in amplitude for the motor and post-motor potential during later hours of the day. Evaluation in source space displayed an increase in the activity of M1 of the contralateral hemisphere and the SMA of both hemispheres until 8 p.m. followed by a decline until midnight. Furthermore, we investigated how changes over time in MRCP dynamics affect the ability to decode motor information. This was achieved by developing classification schemes to assess performance across different scenarios. The observed variations in classification accuracies over time strongly indicate the need for adaptive decoders. Such adaptive decoders would be instrumental in delivering robust results, essential for the practical application of BCIs during day and nighttime usage.
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Electroencefalografía , Gestos , Mano , Humanos , Electroencefalografía/métodos , Masculino , Femenino , Mano/fisiología , Adulto , Adulto Joven , Movimiento/fisiología , Corteza Motora/fisiología , Interfaces Cerebro-ComputadorRESUMEN
BACKGROUND: Eye tracking technology not only reveals the acquisition of visual information at fixation but also has the potential to unveil underlying cognitive processes involved in learning to use a multifunction prosthetic hand. It also reveals gaze behaviours observed during standardized tasks and self-chosen tasks. The aim of the study was to explore the use of eye tracking to track learning progress of multifunction hands at two different time points in prosthetic rehabilitation. METHODS: Three amputees received control training of a multifunction hand with new control strategy. Detailed description of control training was collected first. They wore Tobii Pro2 eye-tracking glasses and performed a set of standardized tasks (required to switch to different grips for each task) after one day of training and at one-year-follow-up (missing data for Subject 3 at the follow up due to socket problem). They also performed a self-chosen task (free to use any grip for any object) and were instructed to perform the task in a way how they would normally do at home. The gaze-overlaid videos were analysed using the Tobii Pro Lab and the following metrics were extracted: fixation duration, saccade amplitude, eye-hand latency, fixation count and time to first fixation. RESULTS: During control training, the subjects learned 3 to 4 grips. Some grips were easier, and others were more difficult because they forgot or were confused with the switching strategies. At the one-year-follow-up, a decrease in performance time, fixation duration, eye-hand latency, and fixation count was observed in Subject 1 and 2, indicating an improvement in the ability to control the multifunction hand and a reduction of cognitive load. An increase in saccade amplitude was observed in both subjects, suggesting a decrease in difficulty to control the prosthetic hand. During the standardized tasks, the first fixation of all three subjects were on the multifunction hand in all objects. During the self-chosen tasks, the first fixations were mostly on the objects first. CONCLUSION: The qualitative data from control training and the quantitative eye tracking data from clinical standardized tasks provided a rich exploration of cognitive processing in learning to control a multifunction hand. Many prosthesis users prefer multifunction hands and with this study we have demonstrated that a targeted prosthetic training protocol with reliable assessment methods will help to lay the foundation for measuring functional benefits of multifunction hands.
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Miembros Artificiales , Tecnología de Seguimiento Ocular , Mano , Aprendizaje , Humanos , Masculino , Mano/fisiología , Adulto , Amputados/rehabilitación , Persona de Mediana Edad , Femenino , Fijación Ocular/fisiología , Desempeño Psicomotor/fisiologíaRESUMEN
Hand motor impairment has seriously affected the daily life of the elderly. We developed an electromyography (EMG) exosuit system with bidirectional hand support for bilateral coordination assistance based on a dynamic gesture recognition model using graph convolutional network (GCN) and long short-term memory network (LSTM). The system included a hardware subsystem and a software subsystem. The hardware subsystem included an exosuit jacket, a backpack module, an EMG recognition module, and a bidirectional support glove. The software subsystem based on the dynamic gesture recognition model was designed to identify dynamic and static gestures by extracting the spatio-temporal features of the patient's EMG signals and to control glove movement. The offline training experiment built the gesture recognition models for each subject and evaluated the feasibility of the recognition model; the online control experiments verified the effectiveness of the exosuit system. The experimental results showed that the proposed model achieve a gesture recognition rate of 96.42% ± 3.26 %, which is higher than the other three traditional recognition models. All subjects successfully completed two daily tasks within a short time and the success rate of bilateral coordination assistance are 88.75% and 86.88%. The exosuit system can effectively help patients by bidirectional hand support strategy for bilateral coordination assistance in daily tasks, and the proposed method can be applied to various limb assistance scenarios.
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Electromiografía , Gestos , Mano , Humanos , Mano/fisiología , Masculino , Femenino , Dispositivo Exoesqueleto , Adulto , Algoritmos , Redes Neurales de la Computación , Reconocimiento de Normas Patrones Automatizadas/métodos , Programas Informáticos , Actividades Cotidianas , Adulto Joven , Estudios de FactibilidadRESUMEN
Brain-computer interfaces (BCIs) are pivotal in translating neural activities into control commands for external assistive devices. Non-invasive techniques like electroencephalography (EEG) offer a balance of sensitivity and spatial-temporal resolution for capturing brain signals associated with motor activities. This work introduces MOVING, a Multi-Modal dataset of EEG signals and Virtual Glove Hand Tracking. This dataset comprises neural EEG signals and kinematic data associated with three hand movements-open/close, finger tapping, and wrist rotation-along with a rest period. The dataset, obtained from 11 subjects using a 32-channel dry wireless EEG system, also includes synchronized kinematic data captured by a Virtual Glove (VG) system equipped with two orthogonal Leap Motion Controllers. The use of these two devices allows for fast assembly (â¼1 min), although introducing more noise than the gold standard devices for data acquisition. The study investigates which frequency bands in EEG signals are the most informative for motor task classification and the impact of baseline reduction on gesture recognition. Deep learning techniques, particularly EEGnetV4, are applied to analyze and classify movements based on the EEG data. This dataset aims to facilitate advances in BCI research and in the development of assistive devices for people with impaired hand mobility. This study contributes to the repository of EEG datasets, which is continuously increasing with data from other subjects, which is hoped to serve as benchmarks for new BCI approaches and applications.
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Interfaces Cerebro-Computador , Electroencefalografía , Mano , Movimiento , Humanos , Electroencefalografía/métodos , Mano/fisiología , Movimiento/fisiología , Masculino , Adulto , Fenómenos Biomecánicos/fisiología , Femenino , Procesamiento de Señales Asistido por ComputadorRESUMEN
Medication adherence is an essential aspect of healthcare for patients and is important for achieving medical objectives. However, the lack of standard techniques for measuring adherence is a global concern, making it challenging to accurately monitor and measure patient medication regimens. The use of sensor technology for medication adherence monitoring has received much attention lately since it makes it possible to continuously observe patients' medication adherence behavior. Sensor devices or smart wearables utilize state-of-the-art machine learning (ML) methods to analyze intricate data patterns and provide predictions accurately. The key aim of this work is to develop a sensor-based hand gesture recognition model to predict medication activities. In this research, a smart sensor device-based hand gesture prediction model is developed to recognize medication intake activities. The device includes a tri-axial gyroscope, geometric, and accelerometer sensors to sense and gather data from hand gestures. A smartphone application gathers hand gesture data from the sensor device, which is then stored in the cloud database in a .csv format. These data are collected, processed, and classified to recognize the medication intake activity using the proposed novel neural network model called Sea Horse Optimization-Deep Neural Network (SHO-DNN). The SHO technique is implemented to update the biases and weights and the number of hidden layers in the DNN model. By updating these parameters, the DNN model is improved in classifying the samples of hand gestures to identify the medication activities. The research model demonstrates impressive performance, with an accuracy of 98.59%, sensitivity of 97.82%, precision of 98.69%, and an F1 score of 98.48%. Hence, the proposed model outperformed the most available models in all the aforementioned aspects. The results indicate that this model is a promising approach for medication adherence monitoring in healthcare applications, instilling confidence in its effectiveness.
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Gestos , Mano , Cumplimiento de la Medicación , Redes Neurales de la Computación , Humanos , Mano/fisiología , Teléfono Inteligente , Dispositivos Electrónicos Vestibles , Algoritmos , Aplicaciones Móviles , Aprendizaje AutomáticoRESUMEN
This study delves into decoding hand gestures using surface electromyography (EMG) signals collected via a precision Myo-armband sensor, leveraging machine learning algorithms. The research entails rigorous data preprocessing to extract features and labels from raw EMG data. Following partitioning into training and testing sets, four traditional machine learning models are scrutinized for their efficacy in classifying finger movements across seven distinct gestures. The analysis includes meticulous parameter optimization and five-fold cross-validation to evaluate model performance. Among the models assessed, the Random Forest emerges as the top performer, consistently delivering superior precision, recall, and F1-score values across gesture classes, with ROC-AUC scores surpassing 99%. These findings underscore the Random Forest model as the optimal classifier for our EMG dataset, promising significant advancements in healthcare rehabilitation engineering and enhancing human-computer interaction technologies.
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Algoritmos , Electromiografía , Gestos , Mano , Aprendizaje Automático , Humanos , Electromiografía/métodos , Mano/fisiología , Masculino , Femenino , Adulto , Procesamiento de Señales Asistido por Computador , Adulto Joven , Reconocimiento de Normas Patrones Automatizadas/métodos , Movimiento/fisiologíaRESUMEN
This study evaluates the effects of object weight and hand dominance on the end-point kinematics of the hand-to-mouth (withdrawal) movement in a functional reach-to-drink task for typically developing school-aged children. Using 3D motion capture, speed (average velocity and peak velocity), straightness (ratio), and smoothness (number of velocity peaks and log dimensionless jerk) of hand movements were calculated for the withdrawal motion with three different bottle weights (empty, half-filled, and full). Average velocity (550.4 ± 142.0 versus 512.1 ± 145.6 mm/s) and peak velocity (916.3 ± 234 versus 842.7 ± 198.4 mm/s) were significantly higher with the empty versus half-filled bottle and with the non-dominant (average: 543.5 ± 145.2 mm/s; peak: 896.5 ± 207 mm/s) versus dominant (average: 525.2 ± 40.7 mm/s; peak: 864.2 ± 209.2 mm/s) hand. There were no differences in straightness or smoothness. These findings indicate that increasing weight in reach-to-drink task puts greater constraints on the task. The slower movements with the dominant hand might denote better precision control than the non-dominant hand. The quantitative motion capture results show average values for the kinematic variables for a functional reach-to-drink task in a typically developing population of school-aged children with changing weights of the bottles that are relevant to a real-life scenario. These results could inform the design of individualized therapeutic interventions to improve functional upper-extremity use in children with neurodevelopmental motor disorders.
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Mano , Movimiento , Humanos , Fenómenos Biomecánicos/fisiología , Niño , Masculino , Femenino , Mano/fisiología , Movimiento/fisiología , Lateralidad Funcional/fisiologíaRESUMEN
Surface electromyography (sEMG), a human-machine interface for gesture recognition, has shown promising potential for decoding motor intentions, but a variety of nonideal factors restrict its practical application in assistive robots. In this paper, we summarized the current mainstream gesture recognition strategies and proposed a gesture recognition method based on multimodal canonical correlation analysis feature fusion classification (MCAFC) for a nonideal condition that occurs in daily life, i.e., posture variations. The deep features of the sEMG and acceleration signals were first extracted via convolutional neural networks. A canonical correlation analysis was subsequently performed to associate the deep features of the two modalities. The transformed features were utilized as inputs to a linear discriminant analysis classifier to recognize the corresponding gestures. Both offline and real-time experiments were conducted on eight non-disabled subjects. The experimental results indicated that MCAFC achieved an average classification accuracy, average motion completion rate, and average motion completion time of 93.44%, 94.05%, and 1.38 s, respectively, with multiple dynamic postures, indicating significantly better performance than that of comparable methods. The results demonstrate the feasibility and superiority of the proposed multimodal signal feature fusion method for gesture recognition with posture variations, providing a new scheme for myoelectric control.
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Algoritmos , Electromiografía , Gestos , Mano , Redes Neurales de la Computación , Reconocimiento de Normas Patrones Automatizadas , Postura , Humanos , Postura/fisiología , Mano/fisiología , Masculino , Reconocimiento de Normas Patrones Automatizadas/métodos , Adulto , Femenino , Adulto Joven , Análisis Discriminante , Aprendizaje Profundo , Voluntarios SanosRESUMEN
In this paper, the innovative design of a robotic hand with soft jointed structure is carried out and a tendon-driven mechanism, a master-slave motor coordinated drive mechanism, a thumb coupling transmission mechanism and a thumb steering mechanism are proposed. These innovative designs allow for more effective actuation in each finger, enhancing the load capacity of the robotic hand while maintaining key performance indicators such as dexterity and adaptability. A mechanical model of the robotic finger was made to determine the application limitations and load capacity. The robotic hand was then prototyped for a set of experiments. The experimental results showed that the proposed theoretical model were reliable. Also, the fingertip force of the robotic finger could reach up to 10.3 N, and the load force could reach up to 72.8 N. When grasping target objects of different sizes and shapes, the robotic hand was able to perform the various power grasping and precision grasping in the Cutkosky taxonomy. Moreover, the robotic hand had good flexibility and adaptability by means of adjusting the envelope state autonomously.