Interventions to Change Movement Behaviors After Stroke: A Systematic Review and Meta-analysis.

OBJECTIVE: This systematic review aimed to determine which interventions increase physical activity (PA) and decrease sedentary behavior (SB) based on objective measures of movement behavior in individuals with stroke. DATA SOURCES: The PubMed (Medline), EMBASE, Scopus, CINAHL (EBSCO), and Web of Science databases were searched for articles published up to January 3, 2023. STUDY SELECTION: The StArt 3.0.3 BETA software was used to screen titles, abstracts, and full texts for studies with randomized controlled trial designs; individuals with stroke (≥18 years of age); interventions aimed at increasing PA or decreasing SB; and objective measurement instruments. DATA EXTRACTION: Data extraction was standardized, considering participants and assessments of interest. The risk of bias and quality of evidence of the included studies were assessed. DATA SYNTHESIS: Twenty-eight studies involving 1855 patients were included. Meta-analyses revealed that in the post-stroke acute/subacute phase, exercise interventions combined with behavior change techniques (BCTs) increased both daily steps (standardized mean difference [SMD]=0.65, P=.0002) and time spent on moderate-to-vigorous intensity physical activities (MVPAs) duration of PA (SMD=0.68, P=.0004) with moderate-quality evidence. In addition, interventions based only on BCTs increased PA levels with very low-quality evidence (SMD (low-intensity physical activity)=0.36, P=.02; SMD (MVPA)=0.56, P=.0004) and decreased SB with low-quality evidence (SMD=0.48, P=.03). In the post-stroke chronic phase, there is statistical significance in favor of exercise-only interventions in PA frequency (steps/day) with moderate-quality evidence (SMD=0.68, P=.002). In general, the risk of bias in the included studies was low. CONCLUSIONS: In the acute/subacute phase after stroke, the use of BCTs combined with exercise can increase the number of daily steps and time spent on MVPA. In contrast, in the post-stroke chronic phase, exercise-only interventions resulted in a significant increase in daily steps.

Diffusion-driven fed-batch fermentation in perforated ring flasks.

PURPOSE: Simultaneous membrane-based feeding and monitoring of the oxygen transfer rate shall be introduced to the newly established perforated ring flask, which consists of a cylindrical glass flask with an additional perforated inner glass ring, for rapid bioprocess development. METHODS: A 3D-printed adapter was constructed to enable monitoring of the oxygen transfer rate in the perforated ring flasks. Escherichia coli experiments in batch were performed to validate the adapter. Fed-batch experiments with different diffusion rates and feed solutions were performed. RESULTS: The adapter and the performed experiments allowed a direct comparison of the perforated ring flasks with Erlenmeyer flasks. In batch cultivations, maximum oxygen transfer capacities of 80 mmol L-1 h-1 were reached with perforated ring flasks, corresponding to a 3.5 times higher capacity than in Erlenmeyer flasks. Fed-batch experiments with a feed reservoir concentration of 500 g glucose L-1 were successfully conducted. Based on the oxygen transfer rate, an ammonium limitation could be observed. By adding 40 g ammonium sulfate L-1 to the feed reservoir, the limitation could be prevented. CONCLUSION: The membrane-based feeding, an online monitoring technique, and the perforated ring flask were successfully combined and offer a new and promising tool for screening and process development in biotechnology.

Delirium is associated with low levels of upright activity in geriatric inpatients-results from a prospective observational study.

BACKGROUND: Delirium is common in geriatric inpatients and associated with poor outcomes. Hospitalization is associated with low levels of physical activity. Motor symptoms are common in delirium, but how delirium affects physical activity remains unknown. AIMS: To investigate differences in physical activity between geriatric inpatients with and without delirium. METHODS: We included acutely admitted patients ≥ 75 years in a prospective observational study at a medical geriatric ward at a Norwegian University Hospital. Delirium was diagnosed according to the DSM-5 criteria. Physical activity was measured by an accelerometer-based device worn on the right thigh. The main outcome was time in upright position (upright time) per 24 h (00.00 to 23.59) on the first day of hospitalization with verified delirium status. Group differences were analysed using t test. RESULTS: We included 237 patients, mean age 86.1 years (Standard Deviation (SD) 5.1), and 73 patients (30.8%) had delirium. Mean upright time day 1 for the entire group was 92.2 min (SD 84.3), with 50.9 min (SD 50.7) in the delirium group and 110.6 min (SD 89.7) in the no-delirium group, mean difference 59.7 minutes, 95% Confidence Interval 41.6 to 77.8, p value < 0.001. DISCUSSION: Low levels of physical activity in patients with delirium raise the question if immobilization may contribute to poor outcomes in delirium. Future studies should investigate if mobilization interventions could improve outcomes of delirium. CONCLUSIONS: In this sample of geriatric inpatients, the group with delirium had lower levels of physical activity than the group without delirium.

MXene-based electrochemical devices applied for healthcare applications.

The initial part of the review provides an extensive overview about MXenes as novel and exciting 2D nanomaterials describing their basic physico-chemical features, methods of their synthesis, and possible interfacial modifications and techniques, which could be applied to the characterization of MXenes. Unique physico-chemical parameters of MXenes make them attractive for many practical applications, which are shortly discussed. Use of MXenes for healthcare applications is a hot scientific discipline which is discussed in detail. The article focuses on determination of low molecular weight analytes (metabolites), high molecular weight analytes (DNA/RNA and proteins), or even cells, exosomes, and viruses detected using electrochemical sensors and biosensors. Separate chapters are provided to show the potential of MXene-based devices for determination of cancer biomarkers and as wearable sensors and biosensors for monitoring of a wide range of human activities.

Empowering the Blind: Contactless Activity Recognition with Commodity Software-Defined Radio and Ultra-High-Frequency Radio Frequency Identification.

This study presents a novel computational radio frequency identification (RFID) system designed specifically for assisting blind individuals, utilising software-defined radio (SDR) with coherent detection. The system employs battery-less ultra-high-frequency (UHF) tag arrays in Gen2 RFID systems, enhancing the transmission of sensed information beyond standard identification bits. Our method uses an SDR reader to efficiently manage multiple tags with Gen2 preambles implemented on a single transceiver card. The results highlight the system's real-time capability to detect movements and direction of walking within a four-meter range, indicating significant advances in contactless activity monitoring. This system not only handles the complexities of multiple tag scenarios but also delineates the influence of system parameters on RFID operational efficiency. This study contributes to assistive technology, provides a platform for future advancements aimed at addressing contemporary limitations in pseudo-localisation, and offers a practical, affordable assistance system for blind individuals.

Intelligent Millimeter-Wave System for Human Activity Monitoring for Telemedicine.

Telemedicine has the potential to improve access and delivery of healthcare to diverse and aging populations. Recent advances in technology allow for remote monitoring of physiological measures such as heart rate, oxygen saturation, blood glucose, and blood pressure. However, the ability to accurately detect falls and monitor physical activity remotely without invading privacy or remembering to wear a costly device remains an ongoing concern. Our proposed system utilizes a millimeter-wave (mmwave) radar sensor (IWR6843ISK-ODS) connected to an NVIDIA Jetson Nano board for continuous monitoring of human activity. We developed a PointNet neural network for real-time human activity monitoring that can provide activity data reports, tracking maps, and fall alerts. Using radar helps to safeguard patients' privacy by abstaining from recording camera images. We evaluated our system for real-time operation and achieved an inference accuracy of 99.5% when recognizing five types of activities: standing, walking, sitting, lying, and falling. Our system would facilitate the ability to detect falls and monitor physical activity in home and institutional settings to improve telemedicine by providing objective data for more timely and targeted interventions. This work demonstrates the potential of artificial intelligence algorithms and mmwave sensors for HAR.

Optimizing Epoch Length and Activity Count Threshold Parameters in Accelerometry: Enhancing Upper Extremity Use Quantification in Cerebral Palsy.

Various accelerometry protocols have been used to quantify upper extremity (UE) activity, encompassing diverse epoch lengths and thresholding methods. However, there is no consensus on the most effective approach. The aim of this study was to delineate the optimal parameters for analyzing accelerometry data to quantify UE use in individuals with unilateral cerebral palsy (CP). METHODS: A group of adults with CP (n = 15) participated in six activities of daily living, while a group of children with CP (n = 14) underwent the Assisting Hand Assessment. Both groups performed the activities while wearing ActiGraph GT9X-BT devices on each wrist, with concurrent video recording. Use ratio (UR) derived from accelerometry and video analysis and accelerometer data were compared for different epoch lengths (1, 1.5, and 2 s) and activity count (AC) thresholds (between 2 and 150). RESULTS: In adults, results are comparable across epoch lengths, with the best AC thresholds being ≥ 100. In children, results are similar across epoch lengths of 1 and 1.5 (optimal AC threshold = 50), while the optimal threshold is higher with an epoch length of 2 (AC = 75). CONCLUSIONS: The combination of epoch length and AC thresholds should be chosen carefully as both influence the validity of the quantification of UE use.

Multibiomarker disease activity score: an objective tool for monitoring rheumatoid arthritis? A systematic review and meta-analysis.

OBJECTIVES: The multibiomarker disease activity (MBDA) score is an objective tool for monitoring disease activity in RA. Here we report a systematic review and meta-analysis of the clinical value of the MBDA score in RA. METHODS: We performed a systematic literature search in five medical databases-MEDLINE (via PubMed), Cochrane Library (CENTRAL), Embase, Scopus and Web of Science-from inception to 13 October 2021. Original articles reporting on the performance of the MBDA score's correlation with conventional disease activity measures or the predictive and discriminative values of the MBDA score for radiographic progression, therapy response, remission and relapse were included. RESULTS: Our systematic search provided a total of 1190 records. After selection and citation searches, we identified 32 eligible studies. We recorded moderate correlations between MBDA score and conventional disease activity measures at baseline [correlation (COR) 0.45 (CI 0.28, 0.59), I2 = 71.0% for the 28-joint DAS with CRP (DAS28-CRP) and COR 0.55 (CI 0.19, 0.78), I2 = 0.0% for DAS28 with ESR] and at follow-up [COR 0.44 (CI 0.28, 0.57, I2 = 70.0% for DAS28-CRP) and found that the odds of radiographic progression were significantly higher for patients with a high baseline MBDA score (>44) than for patients with a low baseline MBDA score (<30) [OR 1.03 (CI 1.02-1.05), I2 = 10.0%]. CONCLUSION: The MBDA score might be used as an objective disease activity marker. In addition, it is also a reliable prognostic marker of radiographic progression.

Ultrasonography in the assessment of disease activity in cranial and large-vessel giant cell arteritis: a prospective follow-up study.

OBJECTIVES: We evaluated sensitivity to change and discriminative abilities of vascular US scores in disease monitoring in the follow-up of a prospective cohort of new-onset cranial and large-vessel (LV) GCA patients. METHODS: Baseline and follow-up (8 weeks, 24 weeks and 15 months) US of temporal arteries (TA), carotid and axillary arteries (LV) included assessment of halo and measurement of the intima media complex (IMC). Max IMC, max halo IMC, sum IMC, sum halo IMC, mean IMC, halo count and the Southend halo score were calculated. The provisional OMERACT US score, OGUS, was obtained, taking the average of temporal arteries and axillary arteries IMCs divided by their normal cut-off values. RESULTS: Baseline US was positive in 44/47 patients (72% TA, 72% LV). Sensitivity to change of all composite US scores containing TAs was evident by week 8 onward. LVs responded poorly and new axillary US lesions emerged in six patients despite clinical remission. The OGUS showed a large magnitude of change and is considered the score least prone to potential bias. All TA-based US scores showed moderate-strong correlation with disease activity markers. OGUS, TA halo count, Southend TA halo score, TA sum IMC and TA mean IMC showed potential to discriminate remission and relapse with area under the curve ≥0.8. CONCLUSIONS: The OGUS is suggested as an outcome measurement for the assessment of treatment response in clinical trials. The abilities of US scores to discriminate remission and relapse are encouraging and should be further explored.

Experimental kS estimation: A comparison of methods for Corynebacterium glutamicum from lab to microfluidic scale.

Knowledge about the specific affinity of whole cells toward a substrate, commonly referred to as kS , is a crucial parameter for characterizing growth within bioreactors. State-of-the-art methodologies measure either uptake or consumption rates at different initial substrate concentrations. Alternatively, cell dry weight or respiratory data like online oxygen and carbon dioxide transfer rates can be used to estimate kS . In this work, a recently developed substrate-limited microfluidic single-cell cultivation (sl-MSCC) method is applied for the estimation of kS values under defined environmental conditions. This method is benchmarked with two alternative microtiter plate methods, namely high-frequency biomass measurement (HFB) and substrate-limited respiratory activity monitoring (sl-RA). As a model system, the substrate affinity kS of Corynebacterium glutamicum ATCC 13032 regarding glucose was investigated assuming a Monod-type growth response. A kS of <70.7 mg/L (with 95% probability) with HFB, 8.55 ± 1.38 mg/L with sl-RA, and 2.66 ± 0.99 mg/L with sl-MSCC was obtained. Whereas HFB and sl-RA are suitable for a fast initial kS estimation, sl-MSCC allows an affinity estimation by determining tD at concentrations less or equal to the kS value. Thus, sl-MSCC lays the foundation for strain-specific kS estimations under defined environmental conditions with additional insights into cell-to-cell heterogeneity.

Objective assessment of patients with idiopathic normal pressure hydrocephalus following ventriculoperitoneal shunt placement using activity-monitoring data: pilot study.

OBJECTIVE: Idiopathic normal pressure hydrocephalus (iNPH) results in significant morbidity in the elderly with symptoms of dementia, gait instability, and urinary incontinence. In well-selected patients, ventriculoperitoneal shunt (VPS) placement often results in clinical improvement. Most postshunt assessments of patients rely on subjective scales. The goal of this study was to assess the utility of remote activity monitoring to provide objective evidence of gait improvement following VPS placement for iNPH. METHODS: Patients with iNPH were prospectively enrolled and fitted with 5 activity monitors (on the hip and bilateral thighs and ankles) that they wore for 4 days preoperatively within 30 days of surgery and for 4 days within 30 days postoperatively. Monitors collected continuous data for number of steps, cadence, body position (upright, prone, supine, and lateral decubitus), gait entropy, and the proportion of each day spent active or static. Data were retrieved from the devices and a comparison of pre- and postoperative movement assessment was performed. The gait data were also correlated with formal clinical gait assessments before and after lumbar puncture and with motion analysis laboratory testing at baseline and 1 month and 1 year after VPS placement. RESULTS: Twenty patients fulfilled the inclusion and exclusion criteria (median age 76 years). The baseline median number of daily steps was 1929, the median percentage of the day spent inactive was 70%, the median percentage of the day with a static posture was 95%, the median gait velocity was 0.49 m/sec, and the median number of steps required to turn was 8. There was objective improvement in median entropy from pre- to postoperatively, increasing from 0.6 to 0.8 (p = 0.002). There were no statistically significant differences for any of the remaining variables measured by the activity monitors when comparing the preoperative to the 1-month postoperative time point. All variables from motion analysis testing showed statistically significant differences or a trend toward significance at 1 year after VPS placement. Among the significantly correlated variables at baseline, cadence was inversely correlated with percentage of gait cycle spent in the support phase (contact with ground vs swing phase). CONCLUSIONS: This pilot study suggests that activity monitoring provides an early objective measure of improvement in gait entropy after VPS placement among patients with iNPH, although a more significant improvement was noted on the detailed clinical gait assessments. Further long-term studies are needed to determine the utility of remote monitoring for assessing gait improvement following VPS placement.

Postpartum health is associated with detection of estrus by activity monitors and reproductive performance in dairy cows.

The objective of this prospective observational study was to investigate associations of postpartum health with estrus detection (ED) by activity monitors and pregnancy outcomes in dairy cows. A total of 1,743 Holstein cows from 2 commercial dairy herds in Ontario, Canada were enrolled 3 wk before expected parturition and examined for health variables until 9 wk postpartum. Body condition score (BCS) and lameness were measured at 3 wk prepartum, and serum concentrations of total Ca, haptoglobin (Hp), and nonesterified fatty acids were measured at 2 and 6 ± 2 d in milk (DIM), and blood ß-hydroxybutyrate (BHB) and metritis were assessed at 4, 8, 11, and 15 ± 2 DIM. Cows were examined for purulent vaginal discharge (PVD) and endometritis (ENDO) by endometrial cytology at wk 5, for lameness at wk 3 and 7, for BCS at wk 9 postpartum, and for time to onset of cyclicity by biweekly serum progesterone (P4) measurements. Additional disease data were obtained from farm records. Reproductive management for first AI was primarily based on ED by activity monitors until at least 75 DIM, and cows not detected in estrus were synchronized. Data were analyzed in multivariable logistic or Cox proportional hazards regression models including blood markers, health variables, potential covariates, and herd as a random effect. Estrus was detected in 77% of primiparous and 66% of multiparous cows between 50 or 55 DIM and 75 DIM. In 1,246 cows, the model-predicted probability of ED (percentage point difference) was lower in cows that had retained placenta (-14%), ENDO (-7%), PVD (-8%), delayed cyclicity (no P4 > 1 ng/mL by wk 9; -12%), or ≥0.5-point BCS loss (-14%) compared with cows without each of these risk factors, and it was negatively associated with blood BHB at 15 DIM. Considering only variables measured on farm (not requiring laboratory analysis), the probability of ED was lower (56 vs. 81%) in cows with >1 risk factor compared with cows without risk factors. The predicted probability of pregnancy at first artificial insemination (percentage point difference) was lower in cows that had ENDO (-7%) or PVD (-7%), and negatively associated with serum Hp at 6 ± 2 DIM. In cows detected in estrus by 75 DIM (n = 888), risk factors for reduced pregnancy rate by 250 DIM (adjusted hazard ratio (AHR); 95% confidence intervals) included difficult calving (AHR: 0.67; 0.45 to 1.00), metritis (AHR: 0.79; 0.61 to 1.01), PVD (AHR: 0.79; 0.65 to 0.97), or lameness (AHR: 0.79; 0.62 to 1.01), and it was negatively associated with serum Hp at 6 ± 2 DIM. Monitoring postpartum health may be used to identify cows that are more or less likely to be detected in estrus by activity monitors and to become pregnant in a timely manner. This would support a selective reproductive management program with targeted interventions.

Effect of transition cow health and estrous expression detected by an automated activity monitoring system within 60 days in milk on reproductive performance of lactating Holstein cows.

The objective of this observational study was to evaluate the association of transition cow health and estrous expression, detected by an automated activity monitoring system (Smarttag Neck, Nedap Livestock Management), with reproductive performance in lactating Holstein cows. A total of 3,750 lactating Holstein cows (1,563 primiparous cows and 2,187 multiparous cows) from a commercial dairy farm in Slovakia calving from January 2020 until July 2021 were enrolled on an ongoing basis. Activity data were recorded from d 7 until d 60 postpartum. Within this observational period, cows were classified into 3 categories: (1) no estrus event (Estrus0), (2) 1 estrus event (Estrus1), or (3) 2 or more estrus events (Estrus2+). Transition cow health was assessed by farm personnel within the first 30 d in milk (DIM) using standard operating procedures. Generalized linear mixed models were used to analyze continuous and categorical data. Cox proportional hazard models were used for time to event data. The overall prevalence of anestrus was 20.8%. Multiparous cows had a greater risk for anestrus compared with primiparous cows [odds ratio (OR) = 1.4]. Cows with stillbirth (OR = 1.76), retained placenta (OR = 2.19), puerperal metritis (OR = 1.48), or subclinical ketosis (OR = 1.51) had a greater risk for anestrus. In addition, cows calving in summer (OR = 0.82), autumn (OR = 0.38), or winter (OR = 0.56) had a higher incidence of anestrus than cows calving in spring. Estrous expression from d 7 until d 60 postpartum was associated with estrous duration (DU) and estrous intensity at first artificial insemination (AI). Cows in Estrus0 had the shortest DU at first postpartum AI (9.4 ± 0.18 h) compared with cows in Estrus1 (10.5 ± 0.13 h) and Estrus2+ (11.4 ± 0.12 h). Cows in Estrus2+ had a longer DU at first postpartum AI compared with cows in Estrus1. For Estrus0, Estrus1, and Estrus2+ cows, pregnancy per AI at first service was 42.5%, 50.9%, and 55.4%, respectively. Estrous expression from d 7 until d 60 postpartum was associated with time to first AI and time to pregnancy. Compared with Estrus0 cows, Estrus1 [hazard ratio (HR) = 1.43] and Estrus2+ cows (HR = 1.62) had an increased hazard of being inseminated within 100 DIM. Compared with Estrus2+, Estrus1 cows had a reduced hazard of being inseminated within 100 DIM (HR = 0.89). Compared with Estrus0 cows, Estrus1 (HR = 1.24) and Estrus2+ cows (HR = 1.46) had an increased hazard of becoming pregnant within 200 DIM. Median DIM to pregnancy were 121, 96, and 92 for Estrus0, Estrus1, and Estrus2+ cows, respectively. In conclusion, cows with transition cow disorders (i.e., stillbirth, retained placenta, puerperal metritis, or subclinical ketosis) had a greater chance for anestrus compared with healthy cows. Cows in Estrus0 had reduced estrous expression at first AI and inferior reproductive performance compared with cows that displayed estrous activity from d 7 until d 60.

LocoBox: Modular Hardware and Open-Source Software for Circadian Entrainment and Behavioral Monitoring in Home Cages.

Day-night locomotor activities are the most readily observed outputs of the circadian (~24-h period) clock in many animals. Temporal patterns of the light-dark schedule serve as input to the clock. While circadian activity patterns under various lighting conditions have been observed and documented, the full extent of circadian locomotor activities by genotype and entrainment remains uncharacterized. To facilitate large-scale, parallel cataloging of circadian input-output patterns, we created the LocoBox, an easy-to-construct and easy-to-operate system that can control environmental light with flexible entrainment scenarios combined with the T-cycle and measure locomotor activities in individual home cages. The LocoBox is made using economical, common components, and normal breeding cages can be used for long-term recording. We provide details of the components and blueprints, along with software programs for Arduino and a Python-based graphical user interface (GUI), so that the system can be easily replicated in other laboratories.

Unmanned Aerial Systems and Deep Learning for Safety and Health Activity Monitoring on Construction Sites.

Construction is a highly hazardous industry typified by several complex features in dynamic work environments that have the possibility of causing harm or ill health to construction workers. The constant monitoring of workers' unsafe behaviors and work conditions is considered not only a proactive but also an active method of removing safety and health hazards and preventing potential accidents on construction sites. The integration of sensor technologies and artificial intelligence for computer vision can be used to create a robust management strategy and enhance the analysis of safety and health data needed to generate insights and take action to protect workers on construction sites. This study presents the development and validation of a framework that implements the use of unmanned aerial systems (UASs) and deep learning (DL) for the collection and analysis of safety activity metrics for improving construction safety performance. The developed framework was validated using a pilot case study. Digital images of construction safety activities were collected on active construction sites using a UAS, and the performance of two different object detection deep-learning algorithms/models (Faster R-CNN and YOLOv3) for safety hardhat detection were compared. The dataset included 7041 preprocessed and augmented images with a 75/25 training and testing split. From the case study results, Faster R-CNN showed a higher precision of 93.1% than YOLOv3 (89.8%). The findings of this study show the impact and potential benefits of using UASs and DL in computer vision applications for managing safety and health on construction sites.

The Effect of a Smartphone App with an Accelerometer on the Physical Activity Behavior of Hospitalized Patients: A Randomized Controlled Trial.

Inactive behavior is common in hospitalized patients. This study investigated the effectiveness of using a smartphone app with an accelerometer (Hospital Fit) in addition to usual care physiotherapy on increasing patients' physical activity (PA) behavior. A randomized controlled trial was performed at Maastricht University Medical Centre. Patients receiving physiotherapy while hospitalized at the department of Pulmonology or Internal Medicine were randomized to usual care physiotherapy or using Hospital Fit additionally. Daily time spent walking, standing, and upright (standing/walking) (min) and daily number of postural transitions were measured with an accelerometer between the first and last treatment. Multiple linear regression analysis was performed to determine the association between PA behavior and Hospital Fit use, corrected for functional independence (mILAS). Seventy-eight patients were included with a median (IQR) age of 63 (56-68) years. Although no significant effects were found, a trend was seen in favor of Hospital Fit. Effects increased with length of use. Corrected for functional independence, Hospital Fit use resulted in an average increase of 27.4 min (95% CI: -2.4-57.3) standing/walking on day five and 29.2 min (95% CI: -6.4-64.7) on day six compared to usual care. Hospital Fit appears valuable in increasing PA in functionally independent patients.

Multisensory System for Long-Term Activity Monitoring to Facilitate Aging-in-Place.

Demographic changes and an ageing population require more effective methods to confront the increased prevalence of chronic diseases which generate dependence in older adults as well as an important rise in social expenditure. The challenge is not only to increase life expectancy, but also to ensure that the older adults can fully enjoy that moment in their lives, living where they wish to (private home, nursing home, …). Physical activity (PA) is a representative parameter of a person's state of health, especially when we are getting older, because it plays an important role in the prevention of diseases, and that is the reason why it is promoted in older adults. One of the goals of this work is to assess the feasibility of objectively measuring the PA levels of older adults wherever they live. In addition, this work proposes long-term monitoring that helps to gather daily activity patterns. We fuse inertial measurements with other technologies (WiFi- and ultrasonic-based location) in order to provide not only PA, but also information about the place where the activities are carried out, including both room-level location and precise positioning (depending on the technology used). With this information, we would be able to generate information about the person's daily routines which can be very useful for the early detection of physical or cognitive impairment.

IoT and Deep Learning-Based Farmer Safety System.

Farming is a fundamental factor driving economic development in most regions of the world. As in agricultural activity, labor has always been hazardous and can result in injury or even death. This perception encourages farmers to use proper tools, receive training, and work in a safe environment. With the wearable device as an Internet of Things (IoT) subsystem, the device can read sensor data as well as compute and send information. We investigated the validation and simulation dataset to determine whether accidents occurred with farmers by applying the Hierarchical Temporal Memory (HTM) classifier with each dataset input from the quaternion feature that represents 3D rotation. The performance metrics analysis showed a significant 88.00% accuracy, precision of 0.99, recall of 0.04, F_Score of 0.09, average Mean Square Error (MSE) of 5.10, Mean Absolute Error (MAE) of 0.19, and a Root Mean Squared Error (RMSE) of 1.51 for the validation dataset, 54.00% accuracy, precision of 0.97, recall of 0.50, F_Score of 0.66, MSE = 0.06, MAE = 3.24, and = 1.51 for the Farming-Pack motion capture (mocap) dataset. The computational framework with wearable device technology connected to ubiquitous systems, as well as statistical results, demonstrate that our proposed method is feasible and effective in solving the problem's constraints in a time series dataset that is acceptable and usable in a real rural farming environment for optimal solutions.

An Exploration of Tri-Axial Accelerometers in Assessing the Therapeutic Efficacy of Constraint-Induced Movement Therapy in Children with Unilateral Cerebral Palsy.

This study aimed to elucidate the role of tri-axial accelerometers in assessing therapeutic interventions, specifically constraint-induced movement therapy (CIMT), in children with unilateral cerebral palsy (UCP). The primary focus was understanding the correlation between the actigraphy metrics recorded during CIMT sessions and the resultant therapeutic outcomes. Children with UCP, aged between 4 and 12 years, participated in this study from July 2021 to December 2022. In conjunction with in-clinic sessions, during which participants wore tri-axial accelerometers on both limbs, we analyzed actigraphy data over three days of routine activities pre- and post-CIMT. While not all metrics derived from the accelerometers indicated significant improvements post-intervention, there was a clear trend towards a more balanced usage of both limbs, particularly evident in Axis 3, associated with vertical movement (p = 0.017). Additionally, a discernible correlation was observed between changes in the magnitude ratio derived from actigraphy data during CIMT (Δweek3-week1) and variations in traditional assessments pre- and post-intervention (ΔT0-T1), specifically the Assisting Hand Assessment grasp and release. Using tri-axial accelerometers has helped clarify the potential impacts of CIMT on children with UCP. The preliminary results suggest a possible link between actigraphy metrics taken during CIMT and the subsequent therapeutic outcomes determined by standardized tests.

Preventative Sensor-Based Remote Monitoring of the Diabetic Foot in Clinical Practice.

Diabetes and its complications, particularly diabetic foot ulcers (DFUs), pose significant challenges to healthcare systems worldwide. DFUs result in severe consequences such as amputation, increased mortality rates, reduced mobility, and substantial healthcare costs. The majority of DFUs are preventable and treatable through early detection. Sensor-based remote patient monitoring (RPM) has been proposed as a possible solution to overcome limitations, and enhance the effectiveness, of existing foot care best practices. However, there are limited frameworks available on how to approach and act on data collected through sensor-based RPM in DFU prevention. This perspective article offers insights from deploying sensor-based RPM through digital DFU prevention regimens. We summarize the data domains and technical architecture that characterize existing commercially available solutions. We then highlight key elements for effective RPM integration based on these new data domains, including appropriate patient selection and the need for detailed clinical assessments to contextualize sensor data. Guidance on establishing escalation pathways for remotely monitored at-risk patients and the importance of predictive system management is provided. DFU prevention RPM should be integrated into a comprehensive disease management strategy to mitigate foot health concerns, reduce activity-associated risks, and thereby seek to be synergistic with other components of diabetes disease management. This integrated approach has the potential to enhance disease management in diabetes, positively impacting foot health and the healthspan of patients living with diabetes.