Varying behavior of different window sizes on the classification of static and dynamic physical activities from a single accelerometer

Fida, Benish; Bernabucci, Ivan; Bibbo, Daniele; Conforto, Silvia; Schmid, Maurizio

doi:10.1016/j.medengphy.2015.04.005

Cited by 68 publications

(48 citation statements)

References 31 publications

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“…Our findings regarding the decrease in performance are in line with the recent work by Fida et al [21] who analyzed the effect of varying window size from w = 1 s to 3 s and suggests that 1 s to 2 s window size gives a better tradeoff when analyzing static and dynamic activities. On the contrary, more recently Shoaib et al [22] proposed a system for complex human activity recognition by varying window sizes from 1 s to 30 s and found that increasing window size improves the recognition rate of complex activities.…”

Section: Resultssupporting

confidence: 91%

“…Due to the large diversity in the design process, the existing PAC systems are not directly comparable which hinders the development of new techniques informed by the strengths and the gaps in these systems. Another issue is that most of the existing PAC systems used younger subjects for data collection [3,4,5,6,9,10,13,14,17,21,22] and few systems collected data on older subjects [11,23,24,25,26]. Furthermore, most PAC systems are developed in a controlled environment, which is quite different from real-life conditions [27].…”

Section: Introductionmentioning

confidence: 99%

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Performance Evaluation of State of the Art Systems for Physical Activity Classification of Older Subjects Using Inertial Sensors in a Real Life Scenario: A Benchmark Study

Awais

Palmerini

Bourke

et al. 2016

Sensors

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The popularity of using wearable inertial sensors for physical activity classification has dramatically increased in the last decade due to their versatility, low form factor, and low power requirements. Consequently, various systems have been developed to automatically classify daily life activities. However, the scope and implementation of such systems is limited to laboratory-based investigations. Furthermore, these systems are not directly comparable, due to the large diversity in their design (e.g., number of sensors, placement of sensors, data collection environments, data processing techniques, features set, classifiers, cross-validation methods). Hence, the aim of this study is to propose a fair and unbiased benchmark for the field-based validation of three existing systems, highlighting the gap between laboratory and real-life conditions. For this purpose, three representative state-of-the-art systems are chosen and implemented to classify the physical activities of twenty older subjects (76.4 ± 5.6 years). The performance in classifying four basic activities of daily life (sitting, standing, walking, and lying) is analyzed in controlled and free living conditions. To observe the performance of laboratory-based systems in field-based conditions, we trained the activity classification systems using data recorded in a laboratory environment and tested them in real-life conditions in the field. The findings show that the performance of all systems trained with data in the laboratory setting highly deteriorates when tested in real-life conditions, thus highlighting the need to train and test the classification systems in the real-life setting. Moreover, we tested the sensitivity of chosen systems to window size (from 1 s to 10 s) suggesting that overall accuracy decreases with increasing window size. Finally, to evaluate the impact of the number of sensors on the performance, chosen systems are modified considering only the sensing unit worn at the lower back. The results, similarly to the multi-sensor setup, indicate substantial degradation of the performance when laboratory-trained systems are tested in the real-life setting. This degradation is higher than in the multi-sensor setup. Still, the performance provided by the single-sensor approach, when trained and tested with real data, can be acceptable (with an accuracy above 80%).

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Section: Resultssupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of State of the Art Systems for Physical Activity Classification of Older Subjects Using Inertial Sensors in a Real Life Scenario: A Benchmark Study

Awais

Palmerini

Bourke

et al. 2016

Sensors

View full text Add to dashboard Cite

show abstract

“…RF showed its superior performance in all the considered evaluation metrics, contributing the highest accuracy of 95.72%, which is even higher than the highest accuracy obtained from [155]. It also achieved the best performance in terms of sensitivity, specificity, F-measure and ROC area measures (95.7%, 99.3%, 0.954 and 0.998, respectively).…”

Section: Model Selectionmentioning

confidence: 83%

“…Fida et al [155] presented the impact of window size on the recognition of both short duration, such as sitting, standing and transitions between activities, and long duration activities, such as walking. They found that 1.5 s window size represented the best trade-off value for recognition among activities.…”

Section: Data Segmentationmentioning

confidence: 99%

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Framework for activity sensing in smart home

Ni¹

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The ability to accurately identify the different activities of daily living (ADLs) is considered as one of the basis to foster new technological solutions inside the home, including services to prolong independent living of the elderly. However, the automated detection of activities is a challenging research area. Existing activity monitoring systems suffer from some practical limitations, such as acceptance, due to privacy issues, as well as technical limitations, such as the complexity of activity recognition, imbalanced data classes or lack of common and suitable representations of the context information.This Thesis proposes a framework for sensing activities in smart home. The main novelty of this research work lies in the application of different approaches simultaneously to obtain a more accurate and proper detection of activities in real environments. The final framework that represents the mixture of the aforementioned approaches includes the contributions described in the following. Firstly, a characterization of the main activities considered in smart home scenarios is proposed which are targeted to older people's independent living. Secondly, it is proposed to use an ensemble of heterogeneous classifiers to recognize twelve activities, including very short-duration transitional activities usually discarded in most of the previous works. We also specify a novel segmentation method that dynamically detects the appropriate starting position of windows from accelerometer data, for the purposes of activity recognition. Thirdly, a human activity ontology is presented to support explicit and flexible representation of reasoning based on activity context information. This ontology has been developed by following the NeON methodology and it is linked with existing technologies capable to handle real-life aspects such as time, space, relations between activities, etc. III ResumenLa habilidad de identificar de forma precisa las distintas actividades de la vida diaria (ADL, Activity of Daily Life) es considerada como una de las bases que potencian el desarrollo de nuevas soluciones tecnológicas dentro del hogar, incluyendo los servicios para prolongar la vida independiente de las personas de edad avanzada. Sin embargo la detección automática de actividades es un área que aún está en las fases iniciales de investigación. Los sistemas de monitorización de actividades existentes sufren de limitaciones prácticas como la aceptación de los sensores por parte de los usuarios debido a la pérdida de privacidad, y limitaciones tecnológicas como el reconocimiento de actividades complejas, datos no preparados para poder ser procesados adecuadamente o la falta de esquemas de representación de información de contexto comunes y adecuados Esta tesis presenta un marco de trabajo para la detección de actividades en el hogar digital. La principal novedad de la tesis radica en la aplicación de distintas aproximaciones de forma simultánea para obtener una detección de actividades más precisa y acorde a escenarios reales.El ...

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Fine-Grained Activity Recognition Based on Features of Action Subsegments and Incremental Broad Learning

Chen

Zhu

et al. 2022

Algorithms and Architectures for Parallel Processing

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Varying behavior of different window sizes on the classification of static and dynamic physical activities from a single accelerometer

Cited by 68 publications

References 31 publications

Performance Evaluation of State of the Art Systems for Physical Activity Classification of Older Subjects Using Inertial Sensors in a Real Life Scenario: A Benchmark Study

Performance Evaluation of State of the Art Systems for Physical Activity Classification of Older Subjects Using Inertial Sensors in a Real Life Scenario: A Benchmark Study

Framework for activity sensing in smart home

Fine-Grained Activity Recognition Based on Features of Action Subsegments and Incremental Broad Learning

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