Trained Subjects Research Articles

Acute Effect of Fixed vs. Self-Selected Rest Interval Between Sets on Physiological and Performance-Related Responses.

Background: Although the comparison between self-managed rest and fixed rest periods in subjects experienced in lower-limb strength training has been investigated, the results remain unclear due to controversies among some studies. Therefore, the present study aimed to analyze the role of self-managed rest versus fixed rest in athletic performance, mean propulsive velocity, velocity loss, muscle oxygen saturation, and rest time in trained subjects; Methods: Thirteen subjects with a minimum of one year of training experience (age (years): 26.31 ± 3.84; height (cm): 175.46 ± 5.61; weight (kg): 79.24 ± 6.83) were randomly assigned to two groups (self-selected rest group [SR] = 7 and fixed rest group [FR] = 6). The subjects underwent a session for evaluation (one maximum repetition (1RM) estimation, familiarization, and data collection) and another day for a traditional strength training session for the back squat, consisting of five sets of four repetitions at 80% of 1RM. One group took a fixed 2 min break, while the other group managed their breaks autonomously (resuming when they felt ready to perform the next set at maximum velocity). Mean propulsive velocity (MPV) was monitored using a linear position transducer, and muscle oxygen saturation (SmO2) was measured with a near-infrared spectroscopy device; Results: Significant differences between the groups were found for the rest time between the first and second sets (SR 97.29 ± 23.70 seg vs. FR 120 ± 0.00 seg). However, no differences were found for MPV, velocity loss, or SmO2; Conclusions: Given the similarities in performance and physiological outcomes between fixed and self-selected rest conditions, both can be used equally depending on the preferences and training goals of coaches and athletes.

Appearance-Based Gaze Estimation as a Benchmark for Eye Image Data Generation Methods

Data augmentation is commonly utilized to increase the size and diversity of training sets for deep learning tasks. In this study, we propose a novel application of an existing image generation approach in the domain of realistic eye images that leverages data collected from 40 subjects. This hybrid method combines the benefits of precise control over the image content provided by 3D rendering, while introducing the previously lacking photorealism and diversity into synthetic images through neural style transfer. We prove its general efficacy as a data augmentation tool for appearance-based gaze estimation when generated data are mixed with a sparse train set of real images. It improved the results for 39 out of 40 subjects, with an 11.22% mean and a 19.75% maximum decrease in gaze estimation error, achieving similar metrics for train and held-out subjects. We release our data repository of eye images with gaze labels used in this work for public access.

Older Adult Fall Risk Prediction with Deep Learning and Timed Up and Go (TUG) Test Data.

Falls are a major health hazard for older adults; therefore, in the context of an aging population, predicting the risk of a patient suffering falls in the near future is of great impact for health care systems. Currently, the standard prospective fall risk assessment instrument relies on a set of clinical and functional mobility assessment tools, one of them being the Timed Up and Go (TUG) test. Recently, wearable inertial measurement units (IMUs) have been proposed to capture motion data that would allow for the building of estimates of fall risk. The hypothesis of this study is that the data gathered from IMU readings while the patient is performing the TUG test can be used to build a predictive model that would provide an estimate of the probability of suffering a fall in the near future, i.e., assessing prospective fall risk. This study applies deep learning convolutional neural networks (CNN) and recurrent neural networks (RNN) to build such predictive models based on features extracted from IMU data acquired during TUG test realizations. Data were obtained from a cohort of 106 older adults wearing wireless IMU sensors with sampling frequencies of 100 Hz while performing the TUG test. The dependent variable is a binary variable that is true if the patient suffered a fall in the six-month follow-up period. This variable was used as the output variable for the supervised training and validations of the deep learning architectures and competing machine learning approaches. A hold-out validation process using 75 subjects for training and 31 subjects for testing was repeated one hundred times to obtain robust estimations of model performances At each repetition, 5-fold cross-validation was carried out to select the best model over the training subset. Best results were achieved by a bidirectional long short-term memory (BLSTM), obtaining an accuracy of 0.83 and AUC of 0.73 with good sensitivity and specificity values.

Leg cycling efficiency is unaltered in healthy aging regardless of sex or training status.

Muscular efficiency during exercise has been used to interrogate aspects of human muscle energetics, including mitochondrial coupling and biomechanical efficiencies. Typically, assessments of muscular efficiency have involved graded exercises. Results of previous studies have been interpreted to indicate a decline in exercise efficiency with aging owing to decreased mitochondrial function. However, discrepancies in variables such as exercise stage duration, cycling cadence, and treadmill walking mechanics may have affected interpretations of results. Furthermore, recent data from our lab examining the ATP to oxygen ratio (P:O) in mitochondrial preparations isolated from NIA mouse skeletal muscle showed no change with aging. Thus, we hypothesized that delta efficiency (Δ€) during steady-rate cycling exercise would not be altered in older healthy subjects compared with young counterparts regardless of biological sex or training status. Young (21-35 yr) and older (60-80 yr) men (n = 21) and women (n = 20) underwent continual, progressive leg cycle ergometer tests pedaling at 60 RPM for three stages (35, 60, 85 W) lasting 4 min. Δ€was calculated as: (Δ work accomplished/Δ energy expended). Overall, cycling efficiencies were not significantly different in older compared with young subjects. Similarly, trained subjects did not exhibit significantly different exercise efficiencies compared to untrained. Moreover, there were no differences between men and women. Hence, our results obtained on healthy young and older subjects are interpreted to mean that previous reports of decreased efficiency in older individuals were attributable to metabolic or biomechanical comorbidities, not aging per se.NEW & NOTEWORTHY Muscular power is reduced, but the efficiency of movement is unaltered in healthy aging.

Learner evaluation of an immersive virtual reality mass casualty incident simulator for triage training

BackgroundTo minimize loss of life, modern mass casualty response requires swift identification, efficient triage categorization, and rapid hemorrhage control. Current training methods remain suboptimal. Our objective was to train first responders to triage a mass casualty incident using Virtual Reality (VR) simulation and obtain their impressions of the training’s quality and effectiveness.We trained subjects in a triage protocol called Sort, Assess, Lifesaving interventions, and Treatment and/or Transport (SALT) Triage then had them respond to a terrorist bombing of a subway station using a fully immersive virtual reality simulation. We gathered learner reactions to their virtual reality experience and post-encounter debriefing with a custom electronic survey. The survey was designed to gather information about participants’ demographics and prior experience, including roles, triage training, and virtual reality experience. We then asked them to evaluate the training and encounter and the system’s potential for training others.ResultsWe received 375 completed evaluation surveys from subjects who experienced the virtual reality encounter. Subjects were primarily paramedics, but also included medical learners as well as other emergency medical service (EMS) professionals. Most participants (95%) recommended the experience for other first responders and rated the simulation (95%) and virtual patients (91%) as realistic. Ninety-four percent (94%) of participants rated the virtual reality simulator as “excellent” or “good.” We observed some differences between emergency medical service and medical professionals regarding their prior experience with disaster response training and their opinions on how much the experience contributed to their learning. We observed no differences between subjects with extensive virtual reality experience and those without.ConclusionsOur virtual reality simulator is an automated, customizable, fully immersive virtual reality system for training and assessing personnel in the proper response to a mass casualty incident. Participants perceived the simulator as an adequate alternative to traditional triage and treatment training and believed that the simulator was realistic and effective for training. Prior experience with virtual reality was not a prerequisite for the use of this system.

Research on the Multi level Training Model for Graduate Students Adapting to the New Positioning of Higher Education Institutions

With the rapid development of higher education and the diversification of talent demand in society, the types and positioning of higher education institutions are becoming increasingly diverse. As an important component of higher education, graduate education also needs to adjust and optimize its training mode accordingly. From the perspective of systems science, this article regards the entire process of cultivating professional degree graduate students as a system, from overall analysis to local research, opens up the graduate student training environment, maintains effective collaboration with various educational resources outside the training subject, focuses on the demand for high-level applied specialized talents in coal mining machinery equipment design and manufacturing in the development of the coal industry in Lvliang District, innovates the training mode, and proposes a collaborative training mode for mechanical degree graduate students in Lvliang University. The aim is to explore a multi-level training model for graduate students that adapts to the new positioning of higher education institutions, analyze the problems existing in the current graduate training model, and propose corresponding optimization strategies, in order to provide reference for the reform and development of graduate education in higher education institutions.

Teatr w szkole: efektywna i przyjemna nauka

Introduction: the theatre arts within the school curriculum has sparked a growing interest in schools as they have unique qualities that can help teachers propose teaching methods and strategies to train subjects who are able to identify their feelings in social contexts, but also a way to attract students' attention and at the same time have an enjoyable and entertaining learning experience. Research aim: was to evaluate the effectiveness of using the arts within curricular learning through a literature review. In addition, a questionnaire was administered to primary school teachers in Taranto, Italy, and its province, on the validity of the use of theatre at school. Evidence-based facts: the experience of theatre in education also enables students to explore help-seeking options. Even apart from explicit learning, there is evidence that engaging children and young people with the arts can have a positive impact on their mental health and well-being. Summary: This should be considered as a starting point for incorporating theatre practice into the school learning program, as it appears to be not far removed, in terms of education, from the school needs of the pupils themselves. The teachers interviewed themselves turn out to be, in this sense, still very restrained and tied to curricular activities to the detriment of the implementation of training interventions that are essential and geared to enhancing different skills.

Prevalence and risk factors of training-related abdominal injuries: A multicenter survey study

PurposeThis study aims to identify the prevalence and risk factors of military training-related abdominal injuries and help plan and conduct training properly. MethodsThis questionnaire survey study was conducted from October 2021 to May 2022 among military personnel from 6 military units and 8 military medical centers and participants’ medical records were consulted to identify the training-related abdominal injuries. All the military personnel who ever participated in military training were included. Those who refused to participate in this study or provided an incomplete questionnaire were excluded. The questionnaire collected demographic information, type of abdominal injury, frequency, training subjects, triggers, treatment, and training disturbance. Chi-square test and t-test were used to compare baseline information. Univariate and multivariate regression analyses were used to explore the risk factors associated with military training-related abdominal injuries. ResultsA total of 3058 participants were involved in this study, among which 1797 (58.8%) had suffered training-related abdominal injuries (the mean age was 24.3 years and the service time was 5.6 years), while 1261 (41.2%) had no training-related abdominal injuries (the mean age was 23.1 years and the service time was 4.3 years). There were 546 injured patients (30.4%) suspended the training and 84 (4.6%) needed to be referred to higher-level hospitals. The most common triggers included inadequate warm-up, fatigue, and intense training. The training subjects with the most abdominal injuries were long-distance running (589, 32.8%). Civil servants had the highest rate of abdominal trauma (17.1%). Age ≥ 25 years, military service ≥ 3 years, poor sleep status, and previous abdominal history were independent risk factors for training-related abdominal injury. ConclusionMore than half of the military personnel have suffered military training-related abdominal injuries. Inadequate warm-up, fatigue, and high training intensity are the most common inducing factors. Scientific and proper training should be conducted according to the factors causing abdominal injuries.

Serum miRNA improves the accuracy of a multivariate index assay for triage of an adnexal mass

ObjectiveTo determine whether a multimodal assay combining serum microRNA with protein biomarkers and metadata improves triage assessment of an adnexal mass. MethodsSerum samples from 468 training subjects (191 cancer cases and 277 benign adnexal mass controls or healthy controls) were analyzed for seven protein biomarkers and 180 miRNA. Circulating analyte data were combined with age and menopausal status (metadata) into a neural network model to classify samples as cases or controls. Forward regression with ten-fold cross-validation minimized the dimensionality of the model while maximizing linear separation between cases and controls. Model validation proceeded using both internal (44 cases and 56 controls) and external validation sets (51 cases and 59 controls). ResultsThe total study population comprised 678 subjects, including 286 cases and 392 controls. Overall, 290 (43%) of the subjects were premenopausal. A panel of 10 miRNA delivered optimal performance when combined with protein and metadata features. The combined model improved the Receiver Operator Characteristic Area Under the Curve (ROC AUC) on the internal (AUC = 0.9; 95% CI 0.81–0.95) and external validation sets (AUC = 0.95; 95% CI 0.90–0.98) compared to miRNA alone or proteins plus metadata (without miRNA). On external validation, the combined model offered 92% sensitivity at 80% specificity overall, with 80% and 100% sensitivity for early and late-stage cancers, respectively, including 78% sensitivity for early-stage, serous ovarian cancers and 82% sensitivity for early-stage, non-serous cancers. ConclusionsA multimodal assay combining miRNA with protein biomarkers, age, and menopausal status improves surgical triage of an adnexal mass.

Diagnostic Evaluation of the Contribution of Complementary Training Subjects in the Self-Perception of Competencies in Ethics, Social Responsibility, and Sustainability in Engineering Students

Higher education institutions, as organizations that transform society, have a responsibility to contribute to the construction of a sustainable and resilient world that is aware of the collateral effects of technological advances. This is the initial phase of a research that aims to determine whether subjects in the complementary training area have a significant effect on ethical, social responsibility, and sustainability (ERS) competencies in engineering students at a public higher education institution (HEI). To this end, a quantitative approach was used, adopting a descriptive comparative cross-sectional design and applying a self-perception instrument to a population of 418 students in the first and last semesters of engineering undergraduate programs. As a result, it was found that students that took subjects in the complementary area did not show significant development in social responsibility and sustainability competencies but did in ethical dimensions. Furthermore, in the triad of ERS competencies, there was a significant difference in students over 36 years of age, suggesting that time and academic experience contribute to a greater understanding and application of ethical and moral principles. These results support the idea that ethics and social responsibility are the pillars of sustainability. The findings highlight the importance of intervening in these subjects and complementing them with educational strategies that promote the continuous development of ERS competencies throughout the entire training itinerary, not only in complementary subjects, but as cross-cutting components of all specific engineering discipline subjects.

Label-guided low-rank Approximation for functional brain network learning in identifying subcortical vascular cognitive impairment

Background and ObjectiveFunctional brain network (FBN) has played a pivotal role in unraveling the inherent mechanism of cognition/behavior and investigating neuropsychological disorders. Particularly, growing evidence suggests that FBNs have shown great potential in classifying brain disorders such as subcortical vascular cognitive impairment (SVCI). However, learning a high-quality FBN is still challenging since there is no ground truth. Most traditional methods tend to focus on learning FBNs independently of the downstream task. This practice neglects the valuable label information crucial for accurate classification. Besides, the methodology of constructing brain networks in an isolated, individualistic manner sidesteps the common information among individuals. MethodsTo address these issues, we developed a new FBN joint learning strategy named Label-Guided Low-rank Approximation (LGLA). LGLA integrates label information from training subjects and uses unlabeled testing subjects for auxiliary training, enhancing the discriminative power of FBN features tailored to testing subjects. By enforcing class-specific similarities and differences, and applying a low-rank constraint to capture shared information, LGLA presents an optimization framework that can be effectively solved. ResultsExperimental results demonstrate the effectiveness of our proposed FBN learning method for identifying SVCI. The proposed method achieved an accuracy of 75.70% and an area under the receiver operating characteristic curve (AUC) of 0.8233 in the task of identifying patients with amnestic mild cognitive impairment (aMCI). For identifying patients with non-amnestic mild cognitive impairment (naMCI), it achieved an accuracy of 63.64% and an AUC of 0.7857, while the task of identifying patients with no cognitive impairment (NCI), it achieved an accuracy of 63.64% and an AUC of 0.7917. Additionally, we further explored the brain network features and discovered potential biomarkers for personalized diagnosis. ConclusionThe LGLA method enhances FBN construction and classification accuracy for SVCI diagnosis by effectively integrating the label information and shared structure among subjects.

Privacy-preserving hierarchical federated learning with biosignals to detect drowsiness while driving

AbstractIn response to the global safety concern of drowsiness during driving, the European Union enforces that new vehicles must integrate detection systems compliant with the general data protection regulation. To identify drowsiness patterns while preserving drivers’ data privacy, recent literature has combined Federated Learning (FL) with different biosignals, such as facial expressions, heart rate, electroencephalography (EEG), or electrooculography (EOG). However, existing solutions are unsuitable for drowsiness detection where heterogeneous stakeholders want to collaborate at different levels while guaranteeing data privacy. There is a lack of works evaluating the benefits of using Hierarchical FL (HFL) with EEG and EOG biosignals, and comparing HFL over traditional FL and Machine Learning (ML) approaches to detect drowsiness at the wheel while ensuring data confidentiality. Thus, this work proposes a flexible framework for drowsiness identification by using HFL, FL, and ML over EEG and EOG data. To validate the framework, this work defines a scenario of three transportation companies aiming to share data from their drivers without compromising their confidentiality, defining a two-level hierarchical structure. This study presents three incremental Use Cases (UCs) to assess detection performance: UC1) intra-company FL, yielding a 77.3% accuracy while ensuring the privacy of individual drivers’ data; UC2) inter-company FL, achieving 71.7% accuracy for known drivers and 67.1% for new subjects, ensuring data confidentiality between companies but not intra-organization; and UC3) HFL inter-company, which ensured comprehensive data privacy both within and between companies, with an accuracy of 71.9% for training subjects and 65.5% for new subjects.

Analysis of the effect and influencing factors of a clinical competency-oriented prospective pre-job training programme on the comprehensive ability of new employees in the department of transfusion medicine.

The subject of pre-job training for transfusion service laboratory technicians is very important. The key is how to make a reasonable systematic training programme to improve the effectiveness of training. A prospective training programme was conducted and an assessment was performed at enrollment (baseline) and reassessment after 3-months training, using the same tools with a validated questionnaire. Clinical competency-oriented prospective pre-job training significantly improves the clinical transfusion-related comprehensive skills of new employees. The post-training assessment score was significantly affected by undergraduate major. This study provided a clinical competency-oriented training programme for new employees in the department of transfusion medicine that could effectively enhance their comprehensive abilities.

Characterization and classification of kinesthetic motor imagery levels

Objective. Kinesthetic Motor Imagery (KMI) represents a robust brain paradigm intended for electroencephalography (EEG)-based commands in brain-computer interfaces (BCIs). However, ensuring high accuracy in multi-command execution remains challenging, with data from C3 and C4 electrodes reaching up to 92% accuracy. This paper aims to characterize and classify EEG-based KMI of multilevel muscle contraction without relying on primary motor cortex signals. Approach. A new method based on Hurst exponents is introduced to characterize EEG signals of multilevel KMI of muscle contraction from electrodes placed on the premotor, dorsolateral prefrontal, and inferior parietal cortices. EEG signals were recorded during a hand-grip task at four levels of muscle contraction (0%, 10%, 40%, and 70% of the maximal isometric voluntary contraction). The task was executed under two conditions: first, physically, to train subjects in achieving muscle contraction at each level, followed by mental imagery under the KMI paradigm for each contraction level. EMG signals were recorded in both conditions to correlate muscle contraction execution, whether correct or null accurately. Independent component analysis (ICA) maps EEG signals from the sensor to the source space for preprocessing. For characterization, three algorithms based on Hurst exponents were used: the original (HO), using partitions (HRS), and applying semivariogram (HV). Finally, seven classifiers were used: Bayes network (BN), naive Bayes (NB), support vector machine (SVM), random forest (RF), random tree (RT), multilayer perceptron (MP), and k-nearest neighbors (kNN). Main results. A combination of the three Hurst characterization algorithms produced the highest average accuracy of 96.42% from kNN, followed by MP (92.85%), SVM (92.85%), NB (91.07%), RF (91.07%), BN (91.07%), and RT (80.35%). of 96.42% for kNN. Significance. Results show the feasibility of KMI multilevel muscle contraction detection and, thus, the viability of non-binary EEG-based BCI applications without using signals from the motor cortex.

Transferrable Subject-Independent Feature Representation for Discriminating EEG-Based Brain Signals

Subject-independent Electroencephalography (EEG) recognition remains challenging due to inherent variability of brain anatomy across different subjects. Such variability is further complicated by the Volume Conduction Effect (VCE) that introduces channel-interference noise, exacerbating subject-specific biases in the recorded EEG signals. Existing studies, often relying large datasets and entangled spatial-temporal features, struggle to overcome this bias, particularly in scenarios with limited EEG data. To this end, we propose a Temporal-Connective EEG Representation Learning (TCERL) framework that disentangles temporal and spatial feature learning. TCERL first employs an one-dimensional convolutional network to extract channel-specific temporal features, mitigating channel-interference noise caused by VCE. Building upon these temporal features, TCERL then leverages Graph Neural Networks to extract subject-invariant topological features from a functional brain network, constructed using the channel-specific features as nodes and functional connectivity as the adjacency matrix. This approach allows TCERL to capture robust representations of brain activity patterns that generalize well across different subjects. Our empirical experiment demonstrates that TCERL outperforms state-of-the-art across a range of training subjects on four public benchmarks and is less sensitive to subject variability. The performance gain is highlighted when limited subjects are available, suggesting the robustness and transferability of the proposed method. Source code can be found in: https://github.com/haoweilou/TCERL .

Effects of visual neuroplasticity training on visual perception, visual quality, and macular blood flow in patients with strabismus.

To observe the effects of visual neuroplasticity training on visual perception, visual quality, and macular blood flow in patients with concomitant strabismus postoperatively. In total, 108 patients underwent binocular strabismus correction operation, and some patients underwent neuroplasticity training. All patients underwent clinical ophthalmic examination, including measurement of best-corrected visual acuity, spherical equivalent, axis length, optical coherence tomography angiography, optical quality analysis system, and visual perception examinations. A total of 78 patients received neuroplasticity training for 1 month postoperatively, and 30 patients did not receive training. All patients underwent a visual perception examination preoperatively and at 1 day, 1 week, and 1 month postoperatively. Macular blood flow and visual quality were examined preoperatively and at 1 month postoperatively. Postoperative visual perception was better than preoperative visual perception (P < 0.05). After neuroplasticity training, the visual perception of the trained subjects was better than that of the untrained subjects (P < 0.05), and the blood flow in the macular area of the trained patients was lower than that of the untrained subjects (P < 0.05). The visual quality of the untrained subjects was lower than that of the trained patients (P < 0.05). Visual inspection system could accurately evaluate binocular visual perception in patients with concomitant strabismus. After surgical alignment of the strabismus patient, training can stimulate and integrate the formation of stereovision in a short period of time, maintain the visual quality of patients after surgery, and provide conditions for the formation of binocular visual signals and binocular stereovision, but in the short term, it will lead to the decrease of macular blood vessel density and perfusion density. However, the long-term effects of training have not been proven.

Subject-Independent Model for Reconstructing Electrocardiography Signals from Photoplethysmography Signals

Electrocardiography (ECG) is the gold standard for monitoring vital signs and for diagnosing, controlling, and preventing cardiovascular diseases (CVDs). However, ECG requires continuous user participation, and cannot be used for continuous cardiac monitoring. In contrast to ECG, photoplethysmography (PPG) devices do not require continued user involvement, and can offer ongoing and long-term detection capabilities. However, from a medical perspective, ECG can provide more information about the heart. Currently, most existing work contains different signals recorded from the same subject in training and test sets. This study proposes a neural network model based on a 1D convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) network. This neural network model can directly reconstruct ECG signals from PPG signals. The learned features are captured from the CNN model and fed into the BiLSTM model. In order to verify the validity of the model, it is evaluated using the MIMIC II dataset in the completely subject-independent model (records are placed in a training set, and a test set appears once, but the test signal belongs to a record that is not in the training set). The Pearson’s correlation coefficient between the reconstructed ECG and the reference ECG of the proposed model is 0.963 in the completely subject-independence model. The results of the proposed model are better than those of several cited state-of-the-art models. The results of our trained model indicate that we can obtain reconstructed ECGs that are highly similar to reference ECGs in the completely subject-independent model.

Boosting brain-computer interface performance through cognitive training: A brain-centric approach

Previous efforts to boost the performance of brain-computer interfaces (BCIs) have predominantly focused on optimizing algorithms for decoding brain signals. However, the untapped potential of leveraging brain plasticity for optimization remains underexplored. In this study, we enhanced the temporal resolution of the human brain in discriminating visual stimuli by eliminating the attentional blink (AB) through color-salient cognitive training, and we confirmed that the mechanism was an attention-based improvement. Using the rapid serial visual presentation (RSVP)-based BCI, we evaluated the behavioral and electroencephalogram (EEG) decoding performance of subjects before and after cognitive training in high target percentage (with AB) and low target percentage (without AB) surveillance tasks, respectively. The results consistently demonstrated significant improvements in the trained subjects. Further analysis indicated that this improvement was attributed to the cognitively trained brain producing more discriminative EEG. Our work highlights the feasibility of cognitive training as a means of brain enhancement to boost BCI performance.

Lifelong Learning in Multi-Purpose Activity of Self-Employed and Employed Persons

Introduction. Managing the lifelong learning environment requires a certain understanding of the educational needs and demands of learners of different categories. However, there is not enough research devoted to the psychological aspects of non-formal and informal learning of working subjects. The aim of the study is to characterize the main forms of learning, inducing and sustaining motivation, engagement and self-regulation of learning among employed and self-employed individuals. Materials and Methods. The study used the methods of theoretical analysis of the problem, testing and survey with the use of educational experience diagnostics methodology and author's questionnaires based on the Informal Learning Questionnaire and Academic Motivation Scale. The empirical sampling encompassed 295 people, including 143 self-employed and 152 employed. Results. The results showed the diversity of forms of lifelong learning used by employed persons, as well as the polymotivated nature of this activity. It was found that professional and cognitive motives are the most pronounced among the employed and self-employed. Respondents are motivated to learning by the desire for self-improvement, professional development objectives, interest in the subject of training, the need for new knowledge and skills. It is proved that self-employed persons, compared to employed persons, have a higher level of involvement, self-regulation of learning and intrinsic motivation focused on professional and personal growth and diverse interests. Discussion and Conclusion. The results of the study expand scientific ideas about lifelong learning and for the first time demonstrate the specificity of involvement and motivation for learning individuals with different employment status. The materials of the article will be useful for researchers, specialists in human resources development, marketers of the education segment, and developers of additional educational programs.

Wearable biofeedback device to assess gait features and improve gait pattern in people with parkinson’s disease: a case series

IntroductionPeople with Parkinson’s Disease (PD) show abnormal gait patterns compromising their independence and quality of life. Among all gait alterations due to PD, reduced step length, increased cadence, and decreased ground-reaction force during the loading response and push-off phases are the most common. Wearable biofeedback technologies offer the possibility to provide correlated single or multi-modal stimuli associated with specific gait events or gait performance, hence promoting subjects’ awareness of their gait disturbances. Moreover, the portability and applicability in clinical and home settings for gait rehabilitation increase the efficiency in the management of PD. The Wearable Vibrotactile Bidirectional Interface (BI) is a biofeedback device designed to extract gait features in real-time and deliver a customized vibrotactile stimulus at the waist of PD subjects synchronously with specific gait phases. The aims of this study were to measure the effect of the BI on gait parameters usually compromised by the typical bradykinetic gait and to assess its usability and safety in clinical practice.MethodsIn this case series, seven subjects (age: 70.4 ± 8.1 years; H&Y: 2.7 ± 0.3) used the BI and performed a test on a 10-meter walkway (10mWT) and a two-minute walk test (2MWT) as pre-training (Pre-trn) and post-training (Post-trn) assessments. Gait tests were executed in random order with (Bf) and without (No-Bf) the activation of the biofeedback stimulus. All subjects performed three training sessions of 40 min to familiarize themselves with the BI during walking activities. A descriptive analysis of gait parameters (i.e., gait speed, step length, cadence, walking distance, double-support phase) was carried out. The 2-sided Wilcoxon sign-test was used to assess differences between Bf and No-Bf assessments (p < 0.05).ResultsAfter training subjects improved gait speed (Pre-trn_No-Bf: 0.72(0.59,0.72) m/sec; Post-trn_Bf: 0.95(0.69,0.98) m/sec; p = 0.043) and step length (Pre-trn_No-Bf: 0.87(0.81,0.96) meters; Post-trn_Bf: 1.05(0.96,1.14) meters; p = 0.023) using the biofeedback during the 10mWT. Similarly, subjects’ walking distance improved (Pre-trn_No-Bf: 97.5 (80.3,110.8) meters; Post-trn_Bf: 118.5(99.3,129.3) meters; p = 0.028) and the duration of the double-support phase decreased (Pre-trn_No-Bf: 29.7(26.8,31.7) %; Post-trn_Bf: 27.2(24.6,28.7) %; p = 0.018) during the 2MWT. An immediate effect of the BI was detected in cadence (Pre-trn_No-Bf: 108(103.8,116.7) step/min; Pre-trn_Bf: 101.4(96.3,111.4) step/min; p = 0.028) at Pre-trn, and in walking distance at Post-trn (Post-trn_No-Bf: 112.5(97.5,124.5) meters; Post-trn_Bf: 118.5(99.3,129.3) meters; p = 0.043). SUS scores were 77.5 in five subjects and 80.3 in two subjects. In terms of safety, all subjects completed the protocol without any adverse events.ConclusionThe BI seems to be usable and safe for PD users. Temporal gait parameters have been measured during clinical walking tests providing detailed outcomes. A short period of training with the BI suggests improvements in the gait patterns of people with PD. This research serves as preliminary support for future integration of the BI as an instrument for clinical assessment and rehabilitation in people with PD, both in hospital and remote environments.Trial registrationThe study protocol was registered (DGDMF.VI/P/I.5.i.m.2/2019/1297) and approved by the General Directorate of Medical Devices and Pharmaceutical Service of the Italian Ministry of Health and by the ethics committee of the Lombardy region (Milan, Italy).