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

Ustad, Astrid; Logacjov, Aleksej; Trollebø, Stine Øverengen; Thingstad, Pernille; Vereijken, Beatrix; Bach, Kerstin; Maroni, Nina Skjæret

doi:10.3390/s23052368

Cited by 14 publications

(9 citation statements)

References 42 publications

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“…These factors have limited the ability and performance of the algorithm. Some newer algorithms incorporate machine learning and artificial intelligence to improve their accuracy [ 20 , 21 ], albeit they might lack the necessary generalisability to be adopted for a wider population. These algorithms could be used to estimate customised thresholds and use additional signal-related features to not only identify SB and PA, but also accurately classify the key postural transitions.…”

Section: Discussionmentioning

confidence: 99%

“…For example, discerning sitting from standing is problematic [ 17 , 18 ]. Studies that employ multiple sensor configurations report better accuracy [ 19 , 20 , 21 ], but such configurations increase the wearability burden especially if used for longer periods of time. Others that employ a single wearable device usually use the thigh as the preferred site (e.g., [ 22 , 23 ]) given that the wrist is preferred for monitoring physical activities [ 24 ]).…”

Section: Introductionmentioning

confidence: 99%

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

Jabbar

Sarvestan

Rehman

et al. 2023

Sensors

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Jabbar

Sarvestan

Rehman

et al. 2023

Sensors

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“…This study took a methodical approach to improve how activities are recognized in older adults, with the main goal of adding AL methods. The dataset is called "HAR70+," it holds important data about activities older adults do daily [30]. This dataset includes walking, shuffling, going up and down stairs, standing, sitting, and lying down.…”

Section: Methodsmentioning

confidence: 99%

“…The classification study reveals diverse performance outcomes among various classifiers for the activity recognition task in four activities: standing, sitting, walking, and lying. 10 compares the accuracy results between the current study and base study [30] on a HAR dataset (HAR70+). The current study reports a higher accuracy than the base.…”

Section: A Performance Analysis Of Activity Recognition Classifiers A...mentioning

confidence: 99%

Advancing Healthcare and Elderly Activity Recognition: Active Machine and Deep Learning for Fine- Grained Heterogeneity Activity Recognition

Abbas,

Sampedro,

Alsubai

et al. 2024

IEEE Access

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This research explores how technology can be used to understand and identify activities among elderly individuals. By utilizing HAR70+ data and applying methods like Active Learning (AL), Machine Learning (ML), and Deep Learning (DL), this research aims to predict various activities performed by older adults. Moreover, the study leverages the HAR70+ dataset, providing insight into the daily activities of older individuals and AL-based ML and DL techniques to construct predictive models for these activities. The research experiments are presented systematically, summarizing the outcomes of various machinelearning models across three iterative experiments. This research explored a diverse array of ML algorithms, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGB) and DL methods such as Deep Neural Networks (DNN) and Long Short-Term Memory networks (LSTM) for experimentation. This research trained models on 7 activities: walking, shuffling, climbing stairs (up and down), standing, sitting, and lying down, and 4 activities separately: standing, sitting, walking, and lying down, using the same method. Results reveal that LSTM achieved the best accuracy of 0.98 for 7 activities and 0.95 using RF on 4 actives, showing the potential of DL and ML techniques, particularly when integrated with AL, to enhance activity recognition rate, patient care, optimize medication strategies and improve the well-being of elderly individuals.

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“…In this research, we used five sensor data attached to the subject's right lower arm, right upper arm, back, right calf, and right thigh. 7) The HAR70 dataset [67] comprises data collected from 18 elderly individuals ranging from 70 to 95 years old. Among these participants, four consistently relied on a walker for ambulation, while one used a walking stick for outdoor activities.…”

Section: ) the Human Activity Recognition Trondheim (Harth)mentioning

confidence: 99%

Adaptive Hierarchical Classification for Human Activity Recognition Using Inertial Measurement Unit (IMU) Time-Series Data

Nematallah,

Rajan

2024

IEEE Access

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Human Activity Recognition (HAR) based on Inertial Measurement Unit (IMU) has become increasingly important in health and fitness applications. These systems can continuously and cost-effectively monitor human activity, regardless of the surrounding environment. However, the current dominant trend in HAR uses black box flat classification (FC) methods, which lack interpretability and do not consider the natural hierarchical relationship between activity classes. Such systems often achieve greater accuracy at the cost of increased complexity and are not suitable for critical decision-making applications. This paper proposes an Adaptive Hierarchical Decision Tree (AHDT) HAR system that recognizes human activities based on IMU measurements along with quasi-stationary inclination feature extraction. The proposed system generates a global classifier that classifies human activities according to a tree taxonomy structure. This approach maintains interpretability while considering the fundamental signal data features embedded in the natural hierarchical representation of the activity classes. In addition to the commonly used flat classification accuracy measures, we applied modified hierarchical accuracy measures to assess the exclusive characteristics of hierarchical relationships between the classes. We used seven publicly available datasets to evaluate the proposed system and compared its performance with other tree-based classifiers, including Random Forest, Gradient Boosting, XGBoost, and AdaBoost classifiers. Our results demonstrated that the AHDT system significantly improves the recognition performance of fine-grained activities and offers a balance between lower complexity and higher interpretability. Overall, the proposed AHDT system provides an interpretable and practical approach to HAR that can be valuable in critical decision-making applications.

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

Cited by 14 publications

References 42 publications

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

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

Advancing Healthcare and Elderly Activity Recognition: Active Machine and Deep Learning for Fine- Grained Heterogeneity Activity Recognition

Adaptive Hierarchical Classification for Human Activity Recognition Using Inertial Measurement Unit (IMU) Time-Series Data

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