Validation of an Activity Type Recognition Model Classifying Daily Physical Behavior in Older Adults: The HAR70+ Model

Daily physical activity and trajectories of care service use among older adults: the HUNT4 Trondheim 70+ study

Introduction

Understanding factors that influence care service use is crucial for developing preventive strategies to maintain independence among older adults. In this study, we aimed to identify distinct trajectory groups of municipal care service use among community-dwelling older adults to determine whether daily physical activity is associated with future care service use.

Methods

This prospective cohort study included 981 community-dwelling older adults from the HUNT4 Trondheim 70+ study. At baseline, physical activity was assessed over seven consecutive days using two accelerometers attached to the thigh and lower back. An activity type machine learning model was used to classify the physical activity types: walking, standing, cycling, running, sitting, and lying. Municipal care service use was retrieved monthly from medical records for 3 years. Using group-based trajectory modeling, we identified distinct trajectories of care service use. Multinomial regression models adjusted for age, sex, education level, dementia, and physical performance were used to evaluate the associations between daily physical activity at baseline and care service group belonging.

Results

We identified four distinct trajectory groups of municipal care service use, labeled steady low (72.7%), low increasing (9.0%), medium increasing (12.0%), and high increasing (6.3%). Daily time spent in total physical activity was not associated with trajectory group belonging when adjusted for age, sex, education level, dementia, and physical performance. However, more time spent walking, in bouts lasting longer than a minute, was associated with a reduced relative risk of belonging to the high increasing compared to the steady low group. Furthermore, age, physical performance, and dementia were all significantly associated with trajectory group belonging, and sex differences were observed. Compared to women, men had a reduced relative risk of belonging to the low increasing, medium increasing, or high increasing trajectory groups.

Conclusion

This study identified four distinct trajectories of municipal care service use among older adults over 3 years. Total daily physical activity was not associated with trajectories of care service use, but more time spent walking in longer bouts was independently associated with lower care service use, even when adjusted for the strong predictors of physical performance, dementia diagnosis, and age.

Physical Activity Patterns Across Life Domains in Chinese Older Adults Aged 60-79 Years - China, 2020

What is already known about this topic?

Physical inactivity among older adults is increasing globally. Analyzing the characteristics and influencing factors of physical activity patterns (PAPs) can inform the design of targeted physical activity (PA) promotion strategies for diverse subgroups.

What is added by this report?

Analysis of national data from 2020 revealed three distinct PAPs among Chinese older adults across life domains: low activity (LA) cluster (53.3%), active leisure (AL) cluster (31.4%), and active home (AH) cluster (15.3%). The AL cluster demonstrated superior psychological status, physical fitness, and built environment conditions compared to both AH and LA clusters. The AH cluster exhibited better physical fitness and built environment characteristics than the LA cluster.

What are the implications for public health practice?

The distinct characteristics among clusters suggest that targeted interventions and policies may be beneficial for each subgroup. These interventions should incorporate enhanced psychosocial support, built environment modifications, and evidence-based guidance for physical fitness improvement, specifically tailored to each cluster's unique characteristics and needs.

Federated Learning for IoMT-Enhanced Human Activity Recognition with Hybrid LSTM-GRU Networks

The proliferation of wearable sensors and mobile devices has fueled advancements in human activity recognition (HAR), with growing importance placed on both accuracy and privacy preservation. In this paper, the author proposes a federated learning framework for HAR, leveraging a hybrid Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) model to enhance feature extraction and classification in decentralized environments. Utilizing three public datasets-UCI-HAR, HARTH, and HAR7+-which contain diverse sensor data collected from free-living activities, the proposed system is designed to address the inherent privacy risks associated with centralized data processing by deploying Federated Averaging for local model training. To optimize recognition accuracy, the author introduces a dual-feature extraction mechanism, combining convolutional blocks for capturing local patterns and a hybrid LSTM-GRU structure to detect complex temporal dependencies. Furthermore, the author integrates an attention mechanism to focus on significant global relationships within the data. The proposed system is evaluated on the three public datasets-UCI-HAR, HARTH, and HAR7+-achieving superior performance compared to recent works in terms of F1-score and recognition accuracy. The results demonstrate that the proposed approach not only provides high classification accuracy but also ensures privacy preservation, making it a scalable and reliable solution for real-world HAR applications in decentralized and privacy-conscious environments. This work showcases the potential of federated learning in transforming human activity recognition, combining advanced feature extraction methodologies and privacy-respecting frameworks to deliver robust, real-time activity classification.

Validation of two novel human activity recognition models for typically developing children and children with Cerebral Palsy

Human Activity Recognition models have potential to contribute to valuable and detailed knowledge of habitual physical activity for typically developing children and children with Cerebral Palsy. The main objective of the present study was to develop and validate two Human Activity Recognition models. One trained on data from typically developing children (n = 63), the second also including data from children with Cerebral Palsy (n = 16), engaging in standardised activities and free play. Our data was collected using accelerometers and ground truth was established with video annotations. Additionally, we aimed to investigate the influence of window settings on model performance. Utilizing the Extreme gradient boost (XGBoost) classifier, twelve sub-models were created, with 1-,3- and 5-seconds windows, with and without overlap. Both Human Activity Recognition models demonstrated excellent predictive capabilities (>92%) for standardised activities for both typically developing and Cerebral Palsy. From all window sizes, the 1-second window performed best for all test groups. Accuracy was slightly lower (>75%) for the Cerebral Palsy test group performing free play activities. The impact of window size and overlap varied depending on activity. In summary both Human Activity Recognition models effectively predict standardised activities, surpassing prior models for typically developing and children with Cerebral Palsy. Notably, the model trained on combined typically developing children and Cerebral Palsy data performed exemplary across all test groups. Researchers should select window settings aligned with their specific research objectives.

Daily physical activity in older adults across levels of care: the HUNT Trondheim 70 + study

BACKGROUND

Physical activity (PA) is imperative for healthy ageing and is a modifiable lifestyle factor. Accurate, clinically meaningful estimates of daily PA among older adults can inform targeted interventions to maintain function and independence. This study describes daily PA in older adults across levels of care as a first step contributing to the limited evidence on potential associations between PA and the use of care services.

METHODS

This study used data from the Trondheim 70 + cohort in the population-based Norwegian HUNT Study. In total, 1042 participants aged 70 years or older with valid activity data were included. PA was assessed using two accelerometers over 7 consecutive days and was classified into PA (walking, standing, running, and cycling) and sedentary behavior (sitting and lying). Data on received care services were retrieved from municipal registers and participants were classified into four levels of care: 1) independently living (81.9%), 2) independently living with low-level home care services (6.5%), 3) recipients of home care services (6.0%), and 4) nursing home residents (5.7%). Time spent in the activity types and duration of bouts are presented across levels of care.

RESULTS

Participants mean age was 77.5 years (range: 70.1-105.4, 55% female) and PA was lower with higher age. Across levels of care, significant group differences were found in the total time spent in PA, particularly in walking and standing. Daily PA, duration of active bouts, and number of daily walking bouts were lower for participants receiving higher levels of care. Standing was the dominant type of PA and walking appeared predominantly in short bouts at all care levels.

CONCLUSIONS

This is the first population-based study using device-measured PA to describe daily PA across levels of care. The results showed that low-intensity activities constitute the primary component of everyday PA, advocating for placing greater emphasis on the significant role these activities play in maintaining daily PA at older age. Furthermore, the study demonstrated that activity types and bout durations are related to the ability to live independently among older adults. Overall, these findings can contribute to better target interventions to maintain function and independence in older adults.

Quantitative Analysis of Mother Wavelet Function Selection for Wearable Sensors-Based Human Activity Recognition

Recent advancements in the Internet of Things (IoT) wearable devices such as wearable inertial sensors have increased the demand for precise human activity recognition (HAR) with minimal computational resources. The wavelet transform, which offers excellent time-frequency localization characteristics, is well suited for HAR recognition systems. Selecting a mother wavelet function in wavelet analysis is critical, as optimal selection improves the recognition performance. The activity time signals data have different periodic patterns that can discriminate activities from each other. Therefore, selecting a mother wavelet function that closely resembles the shape of the recognized activity's sensor (inertial) signals significantly impacts recognition performance. This study uses an optimal mother wavelet selection method that combines wavelet packet transform with the energy-to-Shannon-entropy ratio and two classification algorithms: decision tree (DT) and support vector machines (SVM). We examined six different mother wavelet families with different numbers of vanishing points. Our experiments were performed on eight publicly available ADL datasets: MHEALTH, WISDM Activity Prediction, HARTH, HARsense, DaLiAc, PAMAP2, REALDISP, and HAR70+. The analysis demonstrated in this paper can be used as a guideline for optimal mother wavelet selection for human activity recognition.

Spatiotemporal trends of cardiovascular disease burden attributable to low physical activity during 1990-2019: an analysis of the Global Burden of Disease Study 2019

OBJECTIVES

The epidemiological trends of cardiovascular disease (CVD) burden attributed to low physical activity (LPA) across various regions and countries are poorly understood. Hence, we assessed the global, regional, and national spatiotemporal trends of LPA-related CVD from 1990 to 2019.

STUDY DESIGN

We conducted a secondary analysis of the Global Burden of Disease Study 2019. The data on LPA-related CVD were examined with regard to sex, age, year, and Socio-Demographic Index (SDI).

METHODS

We assessed the temporal changes in age-standardized mortality rate (ASMR) and age-standardized death rate (ASDR) using the estimated annual percentage change (EAPC) over a 30-year period.

RESULTS

There were a staggering 0.64 million deaths and 9.99 million disability-adjusted life-years globally attributed to LPA-related CVD in 2019. The majority of the LPA-related CVD burden was observed in the population aged ≥80 years. It also indicated a high disease burden of LPA-related CVD in Central Asia, Arabian Peninsula, and North Africa. Although there has been a decline in ASMR and ASDR associated with LPA-related CVD on a global scale, the countries experiencing the most substantial increase in LPA-related CVD burden are Uzbekistan, Tajikistan, and Azerbaijan. The ASMR and ASDR remained stable in regions with low, low-middle, and middle SDI levels. The EAPCs of ASMR and ASDR were negatively linked with SDI in 2019.

CONCLUSIONS

From 1990 to 2019, LPA led to a significant and escalating burden of CVD in certain regions, namely, Uzbekistan, Tajikistan, and Azerbaijan. It is imperative for governments and policymakers to implement regulatory measures and strategic interventions aimed at mitigating this burden.

Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults

Accurate measurement of sedentary behaviour in older adults is informative and relevant. Yet, activities such as sitting are not accurately distinguished from non-sedentary activities (e.g., upright activities), especially in real-world conditions. This study examines the accuracy of a novel algorithm to identify sitting, lying, and upright activities in community-dwelling older people in real-world conditions. Eighteen older adults wore a single triaxial accelerometer with an onboard triaxial gyroscope on their lower back and performed a range of scripted and non-scripted activities in their homes/retirement villages whilst being videoed. A novel algorithm was developed to identify sitting, lying, and upright activities. The algorithm's sensitivity, specificity, positive predictive value, and negative predictive value for identifying scripted sitting activities ranged from 76.9% to 94.8%. For scripted lying activities: 70.4% to 95.7%. For scripted upright activities: 75.9% to 93.1%. For non-scripted sitting activities: 92.3% to 99.5%. No non-scripted lying activities were captured. For non-scripted upright activities: 94.3% to 99.5%. The algorithm could, at worst, overestimate or underestimate sedentary behaviour bouts by ±40 s, which is within a 5% error for sedentary behaviour bouts. These results indicate good to excellent agreement for the novel algorithm, providing a valid measure of sedentary behaviour in community-dwelling older adults.