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The value attributed to the forests in developed countries has shifted from commercial wood production towards carbon reduction, habitat for wildlife, scenery and recreation. With the increasing perceived benefits came more concern and effort towards better forest management. There are many research and development initiatives aimed at effectively using the latest technologies to monitor forest environment, forest wildlife, forest incidents such as fires, illegal logging, poaching etc. This paper presents an expert system that we designed, developed and tested for forest environmental protection applications. The system includes remote sensing hubs and a central cloud server with several capabilities that are briefly presented. We detail a new approach of using Laser Imaging Detection and Ranging (LiDAR) sensors placed at ground level to acquire useful data from forest environments. Implementation details and considerations are presented together with test results obtained in a forest environment.

Background Low physical activity (PA) is associated with poor health outcomes after stroke. Step counts are a common metric of PA; however, other physiologic signals (eg, heart rate) may help to identify subgroups of individuals poststroke at varying levels of risk of poor health outcomes. Here, we aimed to identify clinically relevant subgroups of individuals poststroke based on PA profiles that leverage multiple data sources, including step count and heart rate data, from wearable devices. Methods and Results Seventy individuals poststroke participated. Participants wore a Fitbit Inspire 2 for 1 year and completed clinical assessments. We defined a group‐based steps‐per‐minute threshold and an individual heart rate threshold to categorize each minute of PA into 1 of 4 states: high steps/high heart rate, low steps/low heart rate, high steps/low heart rate, and low steps/high heart rate. We used the proportion of time spent in each state along with steps per day, sedentary time, mean steps among minutes with high steps and high heart rate, and resting heart rate in a k‐means clustering algorithm to identify subgroups and compared Activity Measure for Post‐Acute Care Mobility T Score, Stroke Impact Scale, and gait speed among subgroups. We identified 3 subgroups, Active (n=8), Sedentary (n=29), and Deconditioned (n=33), which differed significantly on all clustering variables except resting heart rate. We observed significant differences in Activity Measure for Post‐Acute Care Mobility T scores between subgroups, with the Deconditioned subgroup exhibiting the lowest score. Conclusions Quantifying PA with heart rate and step count using readily available wearable devices can identify clinically meaningful subgroups of individuals poststroke.

Background Alzheimer's disease (AD) affects over 6 million people and is the seventh-leading cause of death in the United States. This study compares wrist-worn accelerometry-derived PA measures against traditional risk factors for incident AD in the UK Biobank. Methods Of 42 157 UK Biobank participants 65 years and older who had accelerometry data and no prior AD diagnosis, 157 developed AD by April 1, 2021 (264 988 person-years or on average 6.2 years of follow-up). Twelve traditional predictors and 8 accelerometer-based physical activity (PA) measures were used in single- and multivariate Cox models. Their predictive performances for future AD diagnosis were compared across models using the repeated cross-validated concordance (rcvC). To account for potential reverse causality, sensitivity analyses were conducted by removing dropouts and cases within the first 6 months, 1 year, and 2 years. Results The best-performing individual predictors of incident AD were age (p  < .0001, rcvC = 0.658) and moderate-to-vigorous PA (MVPA, p  = .0001, rcvC = 0.622). Forward selection produced a model that included age, diabetes, and MVPA (rcvC = 0.681). Adding MVPA to the model containing age and diabetes improved its rcvC from 0.665 to 0.681 (p  = .0030), more than all other potential risk factors considered. Conclusions Objective PA summaries are the best single predictors among modifiable risk factors with a predictive performance close to that of age. Adding PA summaries to traditional risk factors for AD substantially increases the predictive performance of these models. Increasing MVPA by 14.5 minutes per day reduces the hazard substantially, equivalent to 2 years younger. [ABSTRACT FROM AUTHOR]

BackgroundGiven the evolution of processing and analysis methods for accelerometry data over the past decade, it is important to understand how newer summary measures of physical activity compare with established measures. ObjectiveWe aimed to compare objective measures of physical activity to increase the generalizability and translation of findings of studies that use accelerometry-based data. MethodsHigh-resolution accelerometry data from the Baltimore Longitudinal Study on Aging were retrospectively analyzed. Data from 655 participants who used a wrist-worn ActiGraph GT9X device continuously for a week were summarized at the minute level as ActiGraph activity count, monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity. We calculated these measures using open-source packages in R. Pearson correlations between activity count and each measure were quantified both marginally and conditionally on age, sex, and BMI. Each measures pair was harmonized using nonparametric regression of minute-level data. ResultsData were from a sample (N=655; male: n=298, 45.5%; female: n=357, 54.5%) with a mean age of 69.8 years (SD 14.2) and mean BMI of 27.3 kg/m2 (SD 5.0). The mean marginal participant-specific correlations between activity count and monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity were r=0.988 (SE 0.0002324), r=0.867 (SE 0.001841), r=0.913 (SE 0.00132), and r=0.970 (SE 0.0006868), respectively. After harmonization, mean absolute percentage errors of predicting total activity count from monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity were 2.5, 14.3, 11.3, and 6.3, respectively. The accuracies for predicting sedentary minutes for an activity count cut-off of 1853 using monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity were 0.981, 0.928, 0.904, and 0.960, respectively. An R software package called SummarizedActigraphy, with a unified interface for computation of the measures from raw accelerometry data, was developed and published. ConclusionsThe findings from this comparison of accelerometry-based measures of physical activity can be used by researchers and facilitate the extension of knowledge from existing literature by demonstrating the high correlation between activity count and monitor-independent movement summary (and other measures) and by providing harmonization mapping.

Sensor-Cloud Systems (SCS) are designed to link sensor networks and cloud applications, in order to gather and process information. While this is a hot topic for both academia and industry, with a large number of implementations, not much effort was put towards analysing the reliability of these systems. This article presents an experimental implementation of a system in the field of intelligent products and explores reliability improving techniques in five main areas of the SCS: network communication performance, auto recovery, local backup, automated software testing and system security. They all play an important role in determining the level of reliability of the novel Internet of Things (IoT) architecture framework for intelligent products presented in the article. A new formula is proposed for assessing the reliability of a SCS based on metrics from each of the five areas. Metrics used to assess the system reliability are presented, along with comparisons between operating with improvement techniques and without them. The results show that the reliability of the implemented SCS is improved considerably by implementing a deliberate reliability policy using an original five level tiered approach.

The data presented in this article is related to the research article entitled “Longitudinal multiple sclerosis lesion segmentation: Resource and challenge” (Carass et al., 2017) [1]. In conjunction with the 2015 International Symposium on Biomedical Imaging, we organized a longitudinal multiple sclerosis (MS) lesion segmentation challenge providing training and test data to registered participants. The training data consists of five subjects with a mean of 4.4 (±0.55) time-points, and test data of fourteen subjects with a mean of 4.4 (±0.67) time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. The training data including multi-modal scans and manually delineated lesion masks is available for download. In addition, the testing data is also being made available in conjunction with a website for evaluating the automated analysis of the testing data.

We present the package freesurfer, a set of R functions that interface with Freesurfer, a commonly-used open-source software package for processing and analyzing structural neuroimaging data, specifically T1-weighted images. The freesurfer package performs operations on nifti image objects in R using command-line functions from Freesurfer, and returns R objects back to the user. freesurfer allows users to process neuroanatomical images and provides functionality to convert and read the output of the Freesurfer pipelines more easily, including brain images, brain surfaces, and Freesurfer output tables.