Classification of Various Daily Activities using Convolution Neural Network and Smartwatch

Kwon, Min-Cheol; You, Hanjong; Kim, Jeongung; Choi, Sunwoong

doi:10.1109/bigdata.2018.8621893

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…Through the active learning model, 95% of accuracy is proved using 46% fewer samples [1]. Furthermore, in [10], the author proof 97.19% accuracy using a deep learning model through a Convolutional Neural Network (CNN). The classification uses 11 different activities (high or less use in daily life).…”

Section: Related Workmentioning

confidence: 95%

An Analysis of Human Activities Recognition using Smartwatches Dataset

Karim¹,

Burney²,

Mahmood³

2020

IJACSA

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Today, the era of smart devices evolving the human behavior interaction to a changing environment where the learning of activities is monitored to predict the next step of human behavior. The smart devices have these sensors built-in (accelerometer and gyroscope), which are continuously generating a large amount of data. The data used to identify the novel patterns of human behavior, together with machine learning and data mining techniques. Classification of human motions with motion sensor data is among the current topics of study. The classification is an important part of data mining techniques and used in this work to find the accuracy of instances in the given dataset. Thus, it is possible to follow the activities of a user carrying only a smartwatch. The smartwatches consisting of four different models from two manufacturers are used. Furthermore, the experiment contains nine users and seven activities performed by them. After the classification was determined, the data set to which the principal component analysis has been applied was classified by decision stump, j48, Bayes net, naive Bayes, naive Bayes multinomial text, random forest, and logit boost methods, and their performances were compared. The most successful result was obtained from the random forest method. The accuracy of the Random Forest classification algorithm on nominal datasets is 99.99% on both accelerometer and gyroscope sensors.

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Section: Related Workmentioning

confidence: 95%

An Analysis of Human Activities Recognition using Smartwatches Dataset

Karim¹,

Burney²,

Mahmood³

2020

IJACSA

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“…Activities represent units of behavior that can be labeled and integrated into the digital behavior markers. While human activity recognition is a popular research topic and many approaches have been proposed, 32 34 53 54 55 56 57 58 59 most approaches operate under controlled laboratory conditions with scripted, movement-based activities. 60 61 62 63 Research has demonstrated a correlation between cognitive health and numerous activities, both simple and complex, that include sleep, work, time out of the home, walking, and socialization.…”

Section: Methodsmentioning

confidence: 99%

Automated Cognitive Health Assessment Using Partially Complete Time Series Sensor Data

2022

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Background Behavior and health are inextricably linked. As a result, continuous wearable sensor data offer the potential to predict clinical measures. However, interruptions in the data collection occur, which create a need for strategic data imputation. Objective The objective of this work is to adapt a data generation algorithm to impute multivariate time series data. This will allow us to create digital behavior markers that can predict clinical health measures. Methods We created a bidirectional time series generative adversarial network to impute missing sensor readings. Values are imputed based on relationships between multiple fields and multiple points in time, for single time points or larger time gaps. From the complete data, digital behavior markers are extracted and are mapped to predicted clinical measures. Results We validate our approach using continuous smartwatch data for n = 14 participants. When reconstructing omitted data, we observe an average normalized mean absolute error of 0.0197. We then create machine learning models to predict clinical measures from the reconstructed, complete data with correlations ranging from r = 0.1230 to r = 0.7623. This work indicates that wearable sensor data collected in the wild can be used to offer insights on a person's health in natural settings.

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“…While CPAM can provide activity-aware energy reduction for many mobile applications, here, we focus on an activity recognition application. Human activity recognition is a popular research topic [32][33][34][35] and forms a critical component of technologies for health monitoring, intervention, and activity-aware service provisioning [36][37][38]. Additionally, activity recognition provides a vehicle for us to validate our change point detection methods by comparing detected activities with known activity transitions.…”

Section: Monitoring Complex Activitiesmentioning

confidence: 99%

Easing Power Consumption of Wearable Activity Monitoring with Change Point Detection

Culman

Aminikhanghahi

Cook

2020

Sensors

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Continuous monitoring of complex activities is valuable for understanding human behavior and providing activity-aware services. At the same time, recognizing these activities requires both movement and location information that can quickly drain batteries on wearable devices. In this paper, we introduce Change Point-based Activity Monitoring (CPAM), an energy-efficient strategy for recognizing and monitoring a range of simple and complex activities in real time. CPAM employs unsupervised change point detection to detect likely activity transition times. By adapting the sampling rate at each change point, CPAM reduces energy consumption by 74.64% while retaining the activity recognition performance of continuous sampling. We validate our approach using smartwatch data collected and labeled by 66 subjects. Results indicate that change point detection techniques can be effective for reducing the energy footprint of sensor-based mobile applications and that automated activity labels can be used to estimate sensor values between sampling periods.

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Classification of Various Daily Activities using Convolution Neural Network and Smartwatch

Cited by 10 publications

References 14 publications

An Analysis of Human Activities Recognition using Smartwatches Dataset

An Analysis of Human Activities Recognition using Smartwatches Dataset

Automated Cognitive Health Assessment Using Partially Complete Time Series Sensor Data

Easing Power Consumption of Wearable Activity Monitoring with Change Point Detection

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