Mapping features and patterns of accelerometry data on human movement in different age groups and associated health problems: A cross-sectional study.

PURPOSE: Human movement is considered one of the important factors for maintaining an independent life. Individuals in different age groups have different characteristics of locomotion patterns and some health conditions can affect or be affected by mobility changes. Few studies clarify or present data about the influence of different ages and biopsychosocial factors on accelerometry features. The aim of this study was to identify characteristics and variables in the frequency signals for different age groups and their relationship with associated health conditions in raw accelerometry data obtained from the use of a triaxial accelerometer during 7 days of activities of daily living. METHOD: A cross-sectional study was conducted based on the database of the first evaluations of the Epidemiological Study of Movement (EPIMOV) cohort. Frequency, signal amplitude, and entropy accelerometry features of EPIMOV participants who used a triaxial accelerometer for 7 days were extracted. Sociodemographic, clinical, anthropometric and physical activity assessments were also performed. Two-way ANOVA was performed to compare accelerometry features within different age groups. A series of stepwise multiple regressions were performed on accelerometry variables to analyze their relationships with demographic, anthropometric and cardiovascular risk variables. RESULTS: The sample consisted mostly of female, white, and high school graduates. The most prevalent cardiovascular risk factors were sedentary behavior and obesity. When analyzing the accelerometry variables, it was possible to observe that the entropy feature, and the counts, decrease in the group of older adults, while the feature of harmonic components of gait (frequency × amplitude) increases in the group of older adults. Regarding the amplitude feature, there were no significant differences between the groups. Through stepwise multiple linear regression, it was possible to observe that demographic, anthropometric and cardiovascular risk factors are associated with most accelerometry variables. CONCLUSION: The results confirm that human movement can be influenced by different ages, sex, demographic, anthropometric and cardiovascular risk factors. Future studies and clinical analyzes can use the methods proposed in this research to adjust movement patterns for sex and different age groups, thus obtaining new interpretations about human movement.

Diagnosis of Leukaemia in Blood Slides Based on a Fine-Tuned and Highly Generalisable Deep Learning Model.

Leukaemia is a dysfunction that affects the production of white blood cells in the bone marrow. Young cells are abnormally produced, replacing normal blood cells. Consequently, the person suffers problems in transporting oxygen and in fighting infections. This article proposes a convolutional neural network (CNN) named LeukNet that was inspired on convolutional blocks of VGG-16, but with smaller dense layers. To define the LeukNet parameters, we evaluated different CNNs models and fine-tuning methods using 18 image datasets, with different resolution, contrast, colour and texture characteristics. We applied data augmentation operations to expand the training dataset, and the 5-fold cross-validation led to an accuracy of 98.61%. To evaluate the CNNs generalisation ability, we applied a cross-dataset validation technique. The obtained accuracies using cross-dataset experiments on three datasets were 97.04, 82.46 and 70.24%, which overcome the accuracies obtained by current state-of-the-art methods. We conclude that using the most common and deepest CNNs may not be the best choice for applications where the images to be classified differ from those used in pre-training. Additionally, the adopted cross-dataset validation approach proved to be an excellent choice to evaluate the generalisation capability of a model, as it considers the model performance on unseen data, which is paramount for CAD systems.

Foreseeing future falls with accelerometer features in active community-dwelling older persons with no recent history of falls.

BACKGROUND: Acceleration sensors are a viable option for monitoring gait patterns and its application on monitoring falls and risk of falling. However the literature still lacks prospective studies to investigate such risk before the occurrence of falls. OBJECTIVE: To investigate features extracted from accelerometer signals with the purpose of predicting future falls in individuals with no recent history of falls. METHODS: In this study we investigate the risk of fall in active and healthy community-dwelling living older persons with no recent history of falls, using a single accelerometer and variants of the Timed Up and Go (TUG) test. A prospective study was conducted with 74 healthy non-fallers older persons. After collecting acceleration data from the participants at the baseline, the occurrence of falls (outcome) was monitored quarterly during one year. A set of frequency features were extracted from the signal and their ability to predict falls was evaluated. RESULTS: The best individual feature result shows an accuracy of 0.75, sensitivity of 0.71 and specificity of 0.76. A fusion of the three best features increases the sensitivity to 0.86. On the other hand, the cut-off points of the TUG seconds, often used to assess fall risk, did not demonstrate adequate sensitivity. CONCLUSION: The results confirms previous evidence that accelerometer features can better estimate fall risk, and support potential applications that try to infer falls risk in less restricted scenarios, even in a sample stratified by age and gender composed of active and healthy community-dwelling living older persons.

Learning image features with fewer labels using a semi-supervised deep convolutional network.

Learning feature embeddings for pattern recognition is a relevant task for many applications. Deep learning methods such as convolutional neural networks can be employed for this assignment with different training strategies: leveraging pre-trained models as baselines; training from scratch with the target dataset; or fine-tuning from the pre-trained model. Although there are separate systems used for learning features from labelled and unlabelled data, there are few models combining all available information. Therefore, in this paper, we present a novel semi-supervised deep network training strategy that comprises a convolutional network and an autoencoder using a joint classification and reconstruction loss function. We show our network improves the learned feature embedding when including the unlabelled data in the training process. The results using the feature embedding obtained by our network achieve better classification accuracy when compared with competing methods, as well as offering good generalisation in the context of transfer learning. Furthermore, the proposed network ensemble and loss function is highly extensible and applicable in many recognition tasks.

Effects of acute cannabidiol administration on anxiety and tremors induced by a Simulated Public Speaking Test in patients with Parkinson's disease.

BACKGROUND: Cannabidiol (CBD) is one of the main components of Cannabis sativa and has anxiolytic properties, but no study has been conducted to evaluate the effects of CBD on anxiety signs and symptoms in patients with Parkinson's disease (PD). This study aimed to evaluate the impacts of acute CBD administration at a dose of 300 mg on anxiety measures and tremors induced by a Simulated Public Speaking Test (SPST) in individuals with PD. METHODS: A randomised, double-blinded, placebo-controlled, crossover clinical trial was conducted. A total of 24 individuals with PD were included and underwent two experimental sessions within a 15-day interval. After taking CBD or a placebo, participants underwent the SPST. During the test, the following data were collected: heart rate, systemic blood pressure and tremor frequency and amplitude. In addition, the Visual Analog Mood Scales (VAMS) and Self-Statements during Public Speaking Scale were applied. Statistical analysis was performed by repeated-measures analysis of variance (ANOVA) while considering the drug, SPST phase and interactions between these variables. RESULTS: There were statistically significant differences in the VAMS anxiety factor for the drug; CBD attenuated the anxiety experimentally induced by the SPST. Repeated-measures ANOVA showed significant differences in the drug for the variable related to tremor amplitude as recorded by the accelerometer. CONCLUSION: Acute CBD administration at a dose of 300 mg decreased anxiety in patients with PD, and there was also decreased tremor amplitude in an anxiogenic situation.

Fall detection and fall risk assessment in older person using wearable sensors: A systematic review.

BACKGROUND: wearable sensors are often used to acquire data for gait analysis as a strategy to study fall events, due to greater availability of acquisition platforms, and advances in computational intelligence. However, there are no review papers addressing the three most common types of applications related to fall using sensors, namely: fall detection, fallers classification and fall risk screening. OBJECTIVE: To identify the state of art of fall-related events detection in older person using wearable sensors, as well as the main characteristics of the studies in the literature, pointing gaps for future studies. METHODS: A systematic review design was used to search peer-reviewed literature on fall detection and risk in elderly through inertial sensors, published in English, Portuguese, Spanish or French between August 2002 and June 2019. The following questions are investigated: the type of sensors and their sampling rate, the type of signal and data processing employed, the scales and tests used in the study and the type of application. RESULTS: We identified 608 studies, from which 29 were included. The accelerometer, with sampling rate 50 or 100 Hz, allocated in the waist or lumbar was the most used sensor setting. Methods comparing features or variables extracted from the accelerometry signal are the most common, and fall risk screening the most observed application. CONCLUSION: This review identifies the main elements to be addressed in studies on the detection of events related to falls in the elderly and may help in future studies on the subject. However, some aspects are still no reach consensus in the literature such as the size of the sample to be studied, the population under study and how to acquire data for each application.

Better than counting seconds: Identifying fallers among healthy elderly using fusion of accelerometer features and dual-task Timed Up and Go.

Devices and sensors for identification of fallers can be used to implement actions to prevent falls and to allow the elderly to live an independent life while reducing the long-term care costs. In this study we aimed to investigate the accuracy of Timed Up and Go test, for fallers' identification, using fusion of features extracted from accelerometer data. Single and dual tasks TUG (manual and cognitive) were performed by a final sample (94% power) of 36 community dwelling healthy older persons (18 fallers paired with 18 non-fallers) while they wear a single triaxial accelerometer at waist with sampling rate of 200Hz. The segmentation of the TUG different trials and its comparative analysis allows to better discriminate fallers from non-fallers, while conventional functional tests fail to do so. In addition, we show that the fusion of features improve the discrimination power, achieving AUC of 0.84 (Sensitivity = Specificity = 0.83, 95% CI 0.62-0.91), and demonstrating the clinical relevance of the study. We concluded that features extracted from segmented TUG trials acquired with dual tasks has potential to improve performance when identifying fallers via accelerometer sensors, which can improve TUG accuracy for clinical and epidemiological applications.

Precision Agriculture: Using Low-Cost Systems to Acquire Low-Altitude Images.

Low cost remote sensing imagery has the potential to make precision farming feasible in developing countries. In this article, the authors describe image acquisition from eucalyptus, bean, and sugarcane crops acquired by low-cost and low-altitude systems. They use different approaches to handle low-altitude images in both the RGB and NIR (near-infrared) bands to estimate and quantify plantation areas.