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1.
Environ Monit Assess ; 192(12): 774, 2020 Nov 21.
Artigo em Inglês | MEDLINE | ID: mdl-33219863

RESUMO

Vegetation height plays a key role in many environmental applications such as landscape characterization, conservation planning and disaster management, and biodiversity assessment and monitoring. Traditionally, in situ measurements and airborne Light Detection and Ranging (LiDAR) sensors are among the commonly employed methods for vegetation height estimation. However, such methods are known for their high incurred labor, time, and infrastructure cost. The emergence of wearable technology offers a promising alternative, especially in rural environments and underdeveloped countries. A method for a locally designed data acquisition ubiquitous wearable platform has been put forward and implemented. Next, a regression model to learn vegetation height on the basis of attributes associated with a pressure sensor has been developed and tested. The proposed method has been tested in Oulu region. The results have proven particularly effective in a region where the land has a forestry structure. The linear regression model yields (r2 = 0.81 and RSME = 16.73 cm), while the use of a multi-regression model yields (r2 = 0.82 and RSME = 15.73 cm). The developed approach indicates a promising alternative in vegetation height estimation where in situ measurement, LiDAR data, or wireless sensor network is either not available or not affordable, thus facilitating and reducing the cost of ecological monitoring and environmental sustainability planning tasks.


Assuntos
Ecossistema , Dispositivos Eletrônicos Vestíveis , Biodiversidade , Monitoramento Ambiental
2.
Nat Commun ; 11(1): 5615, 2020 11 05.
Artigo em Inglês | MEDLINE | ID: mdl-33154381

RESUMO

Limb motion capture is essential in human motion-recognition, motor-function assessment and dexterous human-robot interaction for assistive robots. Due to highly dynamic nature of limb activities, conventional inertial methods of limb motion capture suffer from serious drift and instability problems. Here, a motion capture method with integral-free velocity detection is proposed and a wearable device is developed by incorporating micro tri-axis flow sensors with micro tri-axis inertial sensors. The device allows accurate measurement of three-dimensional motion velocity, acceleration, and attitude angle of human limbs in daily activities, strenuous, and prolonged exercises. Additionally, we verify an intra-limb coordination relationship exists between thigh and shank in human walking and running, and establish a neural network model for it. Using the intra-limb coordination model, dynamic motion capture of human lower limbs including thigh and shank is tactfully implemented by a single shank-worn device, which simplifies the capture device and reduces cost. Experiments in strenuous activities and long-time running validate excellent performance and robustness of the wearable device in dynamic motion recognition and reconstruction of human limbs.


Assuntos
Extremidades/fisiologia , Monitorização Fisiológica/instrumentação , Movimento (Física) , Dispositivos Eletrônicos Vestíveis , Fenômenos Biomecânicos , Desenho de Equipamento , Humanos , Perna (Membro)/fisiologia , Redes Neurais de Computação , Reprodutibilidade dos Testes , Coxa da Perna/fisiologia
3.
Sensors (Basel) ; 20(21)2020 Oct 29.
Artigo em Inglês | MEDLINE | ID: mdl-33138092

RESUMO

Since its beginning at the end of 2019, the pandemic spread of the severe acute respiratory syndrome coronavirus 2 (Sars-CoV-2) caused more than one million deaths in only nine months. The threat of emerging and re-emerging infectious diseases exists as an imminent threat to human health. It is essential to implement adequate hygiene best practices to break the contagion chain and enhance society preparedness for such critical scenarios and understand the relevance of each disease transmission route. As the unconscious hand-face contact gesture constitutes a potential pathway of contagion, in this paper, the authors present a prototype system based on low-cost depth sensors able to monitor in real-time the attitude towards such a habit. The system records people's behavior to enhance their awareness by providing real-time warnings, providing for statistical reports for designing proper hygiene solutions, and better understanding the role of such route of contagion. A preliminary validation study measured an overall accuracy of 91%. A Cohen's Kappa equal to 0.876 supports rejecting the hypothesis that such accuracy is accidental. Low-cost body tracking technologies can effectively support monitoring compliance with hygiene best practices and training people in real-time. By collecting data and analyzing them with respect to people categories and contagion statistics, it could be possible to understand the importance of this contagion pathway and identify for which people category such a behavioral attitude constitutes a significant risk.


Assuntos
Pessoal de Saúde , Processamento de Imagem Assistida por Computador/métodos , Dispositivos Eletrônicos Vestíveis , Algoritmos , Betacoronavirus/isolamento & purificação , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/prevenção & controle , Infecções por Coronavirus/virologia , Desinfecção/economia , Desinfecção/métodos , Humanos , Processamento de Imagem Assistida por Computador/economia , Processamento de Imagem Assistida por Computador/instrumentação , Saúde do Trabalhador , Pandemias/prevenção & controle , Equipamento de Proteção Individual , Pneumonia Viral/diagnóstico , Pneumonia Viral/prevenção & controle , Pneumonia Viral/virologia
4.
Nat Commun ; 11(1): 4936, 2020 10 06.
Artigo em Inglês | MEDLINE | ID: mdl-33024098

RESUMO

Wearable exercise trackers provide data that encode information on individual running performance. These data hold great potential for enhancing our understanding of the complex interplay between training and performance. Here we demonstrate feasibility of this idea by applying a previously validated mathematical model to real-world running activities of  ≈ 14,000 individuals with ≈ 1.6 million exercise sessions containing duration and distance, with a total distance of ≈ 20 million km. Our model depends on two performance parameters: an aerobic power index and an endurance index. Inclusion of endurance, which describes the decline in sustainable power over duration, offers novel insights into performance: a highly accurate race time prediction and the identification of key parameters such as the lactate threshold, commonly used in exercise physiology. Correlations between performance indices and training volume and intensity are quantified, pointing to an optimal training. Our findings hint at new ways to quantify and predict athletic performance under real-world conditions.


Assuntos
Modelos Teóricos , Corrida/fisiologia , Atletas , Big Data , Exercício Físico/fisiologia , Humanos , Ácido Láctico/metabolismo , Consumo de Oxigênio , Resistência Física/fisiologia , Dispositivos Eletrônicos Vestíveis
5.
J Biomed Opt ; 25(10)2020 10.
Artigo em Inglês | MEDLINE | ID: mdl-33089674

RESUMO

SIGNIFICANCE: The COVID-19 pandemic is changing the landscape of healthcare delivery in many countries, with a new shift toward remote patient monitoring (RPM). AIM: The goal of this perspective is to highlight the existing and future role of wearable and RPM optical technologies in an increasingly at-home healthcare and research environment. APPROACH: First, the specific changes occurring during the COVID-19 pandemic in healthcare delivery, regulations, and technological innovations related to RPM technologies are reviewed. Then, a review of the current state and potential future impact of optical physiological monitoring in portable and wearable formats is outlined. RESULTS: New efforts from academia, industry, and regulatory agencies are advancing and encouraging at-home, portable, and wearable physiological monitors as a growing part of healthcare delivery. It is hoped that these shifts will assist with disease diagnosis, treatment, management, recovery, and rehabilitation with minimal in-person contact. Some of these trends are likely to persist for years to come. Optical technologies already account for a large portion of RPM platforms, with a good potential for future growth. CONCLUSIONS: The biomedical optics community has a potentially large role to play in developing, testing, and commercializing new wearable and RPM technologies to meet the changing healthcare and research landscape in the COVID-19 era and beyond.


Assuntos
Infecções por Coronavirus , Pandemias , Pneumonia Viral , Telemedicina , Dispositivos Eletrônicos Vestíveis , Betacoronavirus , Redes de Comunicação de Computadores , Humanos , Tecnologia de Sensoriamento Remoto
6.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4454-4457, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018983

RESUMO

This paper introduces a low-cost phantom system that simulates fetal movements (FMVs) for the first time. This vibration system can be used for testing wearable inertial sensors which detect FMVs from the abdominal wall. The system consists of a phantom abdomen, a linear stage with a stepper motor, a tactile transducer, and control circuits. The linear stage is used to generate mechanical vibrations which are transferred to the latex abdomen. A tactile transducer is implemented to add environmental noise to the system. The system is characterized and tested using a wireless sensor. The sensor recordings are analyzed using time-frequency analysis and the results are compared to real FMVs reported in the literature. Experiments are conducted to characterize the vibration range, frequency response, and noise generation of the system. It is shown that the system is effective in simulating the vibration of fetal movements, covering the full frequency and magnitude ranges of real FMV vibrations. The noise generation test shows that the system can effectively create scenarios with different signal-to-noise ratios for FMV detection. The system can facilitate the development of fetal movement monitoring systems and algorithms.


Assuntos
Movimento Fetal , Dispositivos Eletrônicos Vestíveis , Humanos , Modalidades de Fisioterapia , Transdutores , Vibração
7.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4644-4647, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019029

RESUMO

The measurement of physiological parameters in sweat has long been assumed to offer a non-invasive alternative to conventional blood testing. Recently, advances in sensor technology enable the production of printed sweat sensors applicable for the use in wearable devices. However, the remaining challenge is the determination of the physiological correlation between blood and sweat components. In this study, we conducted ammonia measurements in blood and sweat during a stepwise incremental cycle ergometer test in 40 subjects under completely controlled conditions in a clinical environment to determine the correlation between the ammonium concentrations in blood and sweat. Samples were taken for each workload step separately. Sweat was sampled directly from the upper body, blood was taken from an indwelling cannula at the end of each workload step, respectively. For meaningful classification of the measured quantities, blood lactate and heart rate were monitored additionally. The results for blood ammonium concentration show increasing behavior in good accordance with the established indicators for physical exhaustion, whereas sweat ammonium concentration seems to decrease with workload. This is found to be due to dilution, as sweat rate increases. The presented results provide insight in the correlation between blood and sweat parameters and therefore are of high importance for further development of wearable devices.Clinical Relevance-Sweat sensing opens up new possibilities for non-invasive, continuous in-situ monitoring of physiological parameters for healthcare and sports science applications.


Assuntos
Compostos de Amônio , Esportes , Dispositivos Eletrônicos Vestíveis , Amônia , Humanos , Suor
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5678-5681, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019265

RESUMO

This paper describes the effects of a smartphone-based wearable telerehabilitation system (called Smarter Balance System, SBS) intended for in-home dynamic weight-shifting balance exercises (WSBEs) by individuals with Parkinson's disease (PD). Two individuals with idiopathic PD performed in-home dynamic WSBEs in anterior-posterior (A/P) and medial-lateral (M/L) directions, using the SBS 3 days per week for 6 weeks. Exercise performance was quantified by cross-correlation (XCORR) and position error (PE) analyses. Balance and gait performance and level of fear of falling were assessed by limit of stability (LOS), Sensory Organization Test (SOT), Falls Efficacy Scale (FES), Activities-specific Balance Confidence (ABC), and Dynamic Gait Index (DGI) at the pre-(beginning of week 1), post-(end of week 6), and retention-(1 month after week 6) assessments. Regression analyses found that exponential trends of the XCORR and PE described exercise performance more effectively than linear trends. Range of LOS in both A/P and M/L directions improved at the post-assessment compared to the pre-assessment, and was retained at the retention assessment. The preliminary findings emphasize the advantages of wearable balance telerehabilitation technologies when performing in-home balance rehabilitation exercises.


Assuntos
Doença de Parkinson , Smartphone , Telerreabilitação , Dispositivos Eletrônicos Vestíveis , Acidentes por Quedas/prevenção & controle , Terapia por Exercício , Medo , Humanos , Equilíbrio Postural
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5798-5801, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019292

RESUMO

Early detection of dementia is becoming increasingly important as it plays a crucial role in handling the patients and offering better treatment. Many of the recent studies concluded a tight relationship between dementia and gait disorders. For this purpose, identification of gait abnormalities is key factor. Novel technologies provide many options such as wearable and non-wearable approaches for analysis of gait. As the occurrence of dementia is more prominent in elderly people, wearable technology is considered out of scope for this work. The gait data of several elderly people over 80 years is acquired over certain intervals during the scope of the project. The elderly people are classified into three study groups namely cognitively healthy individuals (CHI), subjectively cognitively impaired persons (SCI) and possible mildly cognitively impaired persons due to inconclusive test results (pMCI) based on their cognitive status. The gait data is acquired using Kinect sensor. The acquired data consists of both RGB image sequences and depth data of the test persons. 3D human pose estimation is performed on this gait data and gait analysis is done. The transformations in the gait cycles are observed and the health condition of the individual is analyzed. From the analysis, the patterns in the gait abnormalities are correlated with the above-mentioned classification and are used in the detection of dementia in advance. The obtained results look promising and further analysis of gait parameters is under progress.


Assuntos
Demência , Dispositivos Eletrônicos Vestíveis , Idoso , Demência/diagnóstico , Diagnóstico Precoce , Marcha , Análise da Marcha , Humanos
10.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5896-5899, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019316

RESUMO

Predicting mood, health, and stress can sound an early alarm against mental illness. Multi-modal data from wearable sensors provide rigorous and rich insights into one's internal states. Recently, deep learning-based features on continuous high-resolution sensor data have outperformed statistical features in several ubiquitous and affective computing applications including sleep detection and depression diagnosis. Motivated by this, we investigate multi-modal data fusion strategies featuring deep representation learning of skin conductance, skin temperature, and acceleration data to predict self-reported mood, health, and stress scores (0 - 100) of college students (N = 239). Our cross-validated results from the early fusion framework exhibit a significantly higher (p <; 0.05) prediction precision over the late fusion for unseen users. Therefore, our findings call attention to the benefits of fusing physiological data modalities at a low level and corroborate the predictive efficacy of the deeply learned features.


Assuntos
Afeto , Dispositivos Eletrônicos Vestíveis , Humanos , Autorrelato , Temperatura Cutânea , Sono
11.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5935-5938, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019325

RESUMO

Early detection of chronic diseases helps to minimize the disease impact on patient's health and reduce the economic burden. Continuous monitoring of such diseases helps in the evaluation of rehabilitation program effectiveness as well as in the detection of exacerbation. The use of everyday wearables i.e. chest band, smartwatch and smart band equipped with good quality sensor and light weight machine learning algorithm for the early detection of diseases is very promising and holds tremendous potential as they are widely used. In this study, we have investigated the use of acceleration, electrocardiogram, and respiration sensor data from a chest band for the evaluation of obstructive lung disease severity. Recursive feature elimination technique has been used to identity top 15 features from a set of 62 features including gait characteristics, respiration pattern and heart rate variability. A precision of 0.93, recall of 0.91 and F-1 score of 0.92 have been achieved with a support vector machine for the classification of severe patients from the non-severe patients in a data set of 60 patients. In addition, the selected features showed significant correlation with the percentage of predicted FEV1.Clinical Relevance- The study result indicates that wearable sensor data collected during natural walk can be used in the early evaluation of pulmonary patients thus enabling them to seek medical attention and avoid exacerbation. In addition, it may serve as a complementary tool for pulmonary patient evaluation during a 6-minute walk test.


Assuntos
Doença Pulmonar Obstrutiva Crônica , Dispositivos Eletrônicos Vestíveis , Marcha , Humanos , Doença Pulmonar Obstrutiva Crônica/diagnóstico , Teste de Caminhada , Caminhada
12.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5943-5947, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019327

RESUMO

Everyday wearables such as smartwatches or smart bands can play a pivotal role in the field of fitness and wellness and hold the prospect to be used for early disease detection and monitoring towards Smart Health (sHealth). One of the challenges is the extraction of reliable biomarkers from data collected using these devices in the real world (Living Labs). In this yearlong field study, we collected the nocturnal instantaneous heart rate from 9 participants using wrist-worn commercial smart bands and extracted heart rate variability features (HRV). In addition, we measured core body temperature using our custom-designed flexible Inkjet-Printed (IJP) temperature sensor and SpO2 with a finger pulse oximeter. The core body temperature along with user-reported symptoms have been used for automated spatiotemporal monitoring of flu symptoms severity in real-time. The extracted HRV feature values are within the 95% confidence interval of normative values and shows an anticipated trend for gender and age. The resulting dataset from this study is a novel addition and may be used for future investigations.Clinical Relevance- The findings of this study shows usability of wearables in detection and monitoring of diseases such as obstructive sleep apnea reducing the prevalence of undiagnosed cases. This framework also has potentials to monitor outbreaks of flu and other diseases with spatiotemporal distribution.


Assuntos
Dispositivos Eletrônicos Vestíveis , Articulação do Punho , Exercício Físico , Frequência Cardíaca , Humanos , Oximetria
13.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5967-5970, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019331

RESUMO

Artificial intelligence (AI) algorithms including machine and deep learning relies on proper data for classification and subsequent action. However, real-time unsupervised streaming data might not be reliable, which can lead to reduced accuracy or high error rates. Estimating reliability of signals, such as from wearable sensors for disease monitoring, is thus important but challenging since signals can be noisy and vulnerable to artifacts. In this paper, we propose a novel "Data Reliability Metric (DReM)" and demonstrate the proof-of-concept with two bio signals: electrocardiogram (ECG) and photoplethysmogram (PPG). We explored various statistical features and developed Artificial Neural Network (ANN), Random Forest (RF) and Support Vector Machine (SVM) models to autonomously classify good quality signals from the bad quality signals. Our results demonstrate the performance of the classification with a cross-validation accuracy of 99.7%, sensitivity of 100%, precision of 97% and F-score of 96%. This work demonstrates the potential of DReM to objectively and automatically estimate signal quality in unsupervised real-time settings with low computational requirement suitable for low-power digital signal processing techniques on wearables.


Assuntos
Inteligência Artificial , Dispositivos Eletrônicos Vestíveis , Redes Neurais de Computação , Reprodutibilidade dos Testes , Processamento de Sinais Assistido por Computador
14.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5996-6000, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019338

RESUMO

Parkinson's Disease (PD) is considered to be the second most common age-related neuroegenerative disorder, and it is estimated that seven to ten million people worldwide have PD. One of the symptoms of PD is tremor, and studies have shown that wearable assistive devices have the potential to assist in suppressing it. However, despite the progress in the development of these devices, their performance is limited by the tremor estimators they use. Thus, a need for a tremor model that helps the wearable assistive devices to increase tremor suppression without impeding voluntary motion remains. In this work, a user-independent and task-independent tremor and voluntary motion detection method based on neural networks is proposed. Inertial measurement units (IMUs) were used to measure acceleration and angular velocity from participants with PD, these data were then used to train the neural network. The achieved estimation percentage accuracy of voluntary motion was 99.0%, and the future prediction percentage accuracy was 97.3%, 93.7%, 91.4% and 90.3% for 10 ms, 20 ms, 50 ms and 100 ms ahead, respectively. The root mean squared error (RMSE) achieved for tremor estimation was an average of 0.00087°/s on new unseen data, and the future prediction average RMSE across the different tasks achieved was 0.001°/s, 0.002°/s, 0.020°/s and 0.049°/s for 1 ms, 2 ms, 5 ms, and 10 ms ahead, respectively. Therefore, the proposed method shows promise for use in wearable suppression devices.


Assuntos
Doença de Parkinson , Dispositivos Eletrônicos Vestíveis , Humanos , Movimento (Física) , Redes Neurais de Computação , Tremor/diagnóstico
15.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 6001-6004, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019339

RESUMO

Dyskinesias are abnormal involuntary movements that patients with mid-stage and advanced Parkinson's disease (PD) may suffer from. These troublesome motor impairments are reduced by adjusting the dose or frequency of medication levodopa. However, to make a successful adjustment, the treating physician needs information about the severity rating of dyskinesia as patients experience in their natural living environment. In this work, we used movement data collected from the upper and lower extremities of PD patients along with a deep model based on Long Short-Term Memory to estimate the severity of dyskinesia. We trained and validated our model on a dataset of 14 PD subjects with dyskinesia. The subjects performed a variety of daily living activities while their dyskinesia severity was rated by a neurologist. The estimated dyskinesia severity ratings from our developed model highly correlated with the neurologist-rated dyskinesia scores (r=0.86 (p<0.001) and 1.77 MAE (6%)) indicating the potential of the developed the approach in providing the information required for effective medication adjustments for dyskinesia management.


Assuntos
Discinesias , Doença de Parkinson , Dispositivos Eletrônicos Vestíveis , Antiparkinsonianos/efeitos adversos , Discinesias/diagnóstico , Humanos , Levodopa/efeitos adversos , Doença de Parkinson/tratamento farmacológico
17.
Artigo em Inglês | MEDLINE | ID: mdl-33017934

RESUMO

Cardiovascular disease is one of the leading factors for death cause of human beings. In the past decade, heart sound classification has been increasingly studied for its feasibility to develop a non-invasive approach to monitor a subject's health status. Particularly, relevant studies have benefited from the fast development of wearable devices and machine learning techniques. Nevertheless, finding and designing efficient acoustic properties from heart sounds is an expensive and time-consuming task. It is known that transfer learning methods can help extract higher representations automatically from the heart sounds without any human domain knowledge. However, most existing studies are based on models pre-trained on images, which may not fully represent the characteristics inherited from audio. To this end, we propose a novel transfer learning model pre-trained on large scale audio data for a heart sound classification task. In this study, the PhysioNet CinC Challenge Dataset is used for evaluation. Experimental results demonstrate that, our proposed pre-trained audio models can outperform other popular models pre-trained by images by achieving the highest unweighted average recall at 89.7 %.


Assuntos
Meios de Comunicação , Ruídos Cardíacos , Dispositivos Eletrônicos Vestíveis , Acústica , Humanos , Aprendizado de Máquina
18.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 580-583, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018055

RESUMO

Recently, classification from compressed physiological signals in compressed sensing has been successfully applied to cardiovascular disease monitoring. However, in real-time wearable electrocardiogram (ECG) monitoring, it is very difficult to directly obtain the heartbeats information from compressed ECG signals. Thus arrhythmia classification from compressed ECG signals has to be handled in fixed-length segments instead of individual heartbeats. An inevitable issue is that a fixed-length ECG segment may contain multiple different types of arrhythmia. As a result, it is not appropriate to represent the multi-type real arrhythmia with a single label. In this paper, we first introduce multiple labels into fixed-length compressed ECG segments to challenge the arrhythmia classification issue. Then, we propose a deep learning model, which can directly classify multiple different types of arrhythmia from fixed-length compressed ECG segments with the advantages of low time cost for data processing and relatively high classification accuracy at a high compression ratio. Experimental results on the MIT-BIH arrhythmia database show that the exact match rate of our proposed method has reached 96.03% at CR(Compression Ratio)=70%, 94.99% at CR=80% and 93.19% at CR=90%.


Assuntos
Compressão de Dados , Dispositivos Eletrônicos Vestíveis , Arritmias Cardíacas/diagnóstico , Eletrocardiografia , Frequência Cardíaca , Humanos
19.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 584-587, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018056

RESUMO

Sports activity is characterised by the influence of different factors, which relate to both psychological and emotional stress of athletes. As a consequence, mental and physical preparations are fundamental in pre-competition and competition activities. In fact, being able to manage the reactions to stressful events and high demanding conditions, and adapt the strategy depending on the ongoing situation and opponent's reactions allow the athletes to properly process the surrounding information, evaluate all the possible solutions, and finally take the right decision. In this regard, the Skin Conductance (SC), Heart Rate (HR), and Skin Temperature (ST) signals were recorded during a grappling tournament from ten athletes with the aim to investigate if physiological assessments could provide an objective measure of athletes' attitude. The results proved that individual training programs can be tailored accordingly to the neurophysiological state of the athletes, but also that their awareness about both mental and physical preparations and attitudes could be improved.


Assuntos
Esportes , Dispositivos Eletrônicos Vestíveis , Animais , Atletas , Atitude , Frequência Cardíaca , Humanos
20.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 588-591, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018057

RESUMO

Stroke survivors are often characterized by hemiparesis, i.e., paralysis in one half of the body, that severely affects upper limb movements. Continuous monitoring of the progression of hemiparesis requires manual observation of the limb movements at regular intervals and hence is a labour intensive process. In this work, we use wrist-worn accelerometers for automated assessment of hemiparetic severity in acute stroke patients through bivariate Poincaré analysis between accelerometer data from the two hands during spontaneous and instructed movements. Experiments show that while the bivariate Poincaré descriptors CSD1 and CSD2 can identify hemiparetic patients from control subjects, a novel descriptor called Complex Cross-Correlation Measure (C3M) can distinguish between moderate and severe hemiparesis. Further, we justify the use of C3M by showing that it is described by multiple-lag cross-correlations, representing the co-ordination of activity between two hands. The descriptors are compared against the National Institutes of Health Stroke Scale (NIHSS), the clinical gold standard for evaluation of hemiparetic severity, and studied using statistical tests for developing supervised models for hemiparesis classification.Clinical relevance-This study establishes the suitability of wrist-worn accelerometers in identifying hemiparetic severity in stroke patients through novel descriptors of hand co-ordination.


Assuntos
Reabilitação do Acidente Vascular Cerebral , Acidente Vascular Cerebral , Dispositivos Eletrônicos Vestíveis , Acelerometria , Humanos , Paresia/diagnóstico , Acidente Vascular Cerebral/complicações , Estados Unidos
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