scholarly journals Activity recognition based on accelerometer sensor using combinational classifiers

2015 ◽  
Author(s):  
M.N.Shah Zainudin ◽  
Md Nasir Sulaiman ◽  
Norwati Mustapha ◽  
Thinagaran Perumal
Keyword(s):  
Activity Recognition ◽  
Accelerometer Sensor

scholarly journals MULTI-LABEL CLASSIFICATION FOR PHYSICAL ACTIVITY RECOGNITION FROM VARIOUS ACCELEROMETER SENSOR POSITIONS

2018 ◽  
Author(s):  
Raihani Mohamed ◽  
Mohammad Noorazlan Shah Zainudin ◽  
Md. Nasir Sulaiman ◽  
Thinagaran Perumal ◽  
Norwati Mustapha
Keyword(s):  
Activity Recognition ◽  
State Of The Art ◽  
Classification Result ◽  
Accelerometer Sensor ◽  
Classification Technique ◽  
Build Time ◽  
Physical Activity Recognition ◽  
Multi Class Classification ◽  
Sensor Positions

In recent years, the use of accelerometers embedded in smartphones for Human Activity Recognition (HAR) has been well considered. Nevertheless, the role of the sensor placement is yet to  be  explored  and  needs  to  be  further  investigated. In this study, we investigated the role of sensor placements for recognizing various types of physical activities using the accelerometer sensor embedded in the smartphone. In fact, most of the reported work in HAR utilized traditional multi-class classification approaches to determine the types of activities. Hence, this study was to recognize the activity based on the best sensor placements that are appropriate to the activity performed. The traditional multi-class classification approach required more manual work and was time consuming to run the experiment separately. Thus, this study proposed the multi- label classification technique with the Label Combination (LC) approach in order to tackle this issue. The result was compared with several state-of-the-art traditional multi-class classification approaches. The multi-label classification result significantly outperformed the traditional multi-class classification methods as well as minimized the model build time.

Nurse Care Activity Recognition from Accelerometer Sensor Data Using Fourier- and Wavelet-based Features

2021 ◽  
Author(s):  
M. Ashikuzzaman Kowshik ◽  
Yeasin Arafat Pritom ◽  
Md.Sohanur Rahman ◽  
Ali Akbar ◽  
Md Atiqur Rahman Ahad
Keyword(s):  
Activity Recognition ◽  
Sensor Data ◽  
Accelerometer Sensor ◽  
Nurse Care ◽  
Care Activity

scholarly journals An Approach towards Position-Independent Human Activity Recognition Model based on Wearable Accelerometer Sensor

2020 ◽  
Vol 177 ◽  
pp. 196-203
Author(s):  
Naima Qamar ◽  
Nasir Siddiqui ◽  
Muhammad Ehatisham-ul-Haq ◽  
Muhammad Awais Azam ◽  
Usman Naeem
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Accelerometer Sensor ◽  
Recognition Model ◽  
Model Based

scholarly journals Enhancing COVID-19 tracking apps with human activity recognition using a deep convolutional neural network and HAR-images

2021 ◽  
Author(s):  
Gianni D’Angelo ◽  
Francesco Palmieri
Keyword(s):  
Neural Network ◽  
Activity Recognition ◽  
Human Activity ◽  
Information Dissemination ◽  
Distance Estimation ◽  
Real Data ◽  
Human Activity Recognition ◽  
Contact Tracing ◽  
Accelerometer Sensor ◽  
Mobile Health Applications

AbstractWith the emergence of COVID-19, mobile health applications have increasingly become crucial in contact tracing, information dissemination, and pandemic control in general. Apps warn users if they have been close to an infected person for sufficient time, and therefore potentially at risk. The distance measurement accuracy heavily affects the probability estimation of being infected. Most of these applications make use of the electromagnetic field produced by Bluetooth Low Energy technology to estimate the distance. Nevertheless, radio interference derived from numerous factors, such as crowding, obstacles, and user activity can lead to wrong distance estimation, and, in turn, to wrong decisions. Besides, most of the social distance-keeping criteria recognized worldwide plan to keep a different distance based on the activity of the person and on the surrounding environment. In this study, in order to enhance the performance of the COVID-19 tracking apps, a human activity classifier based on Convolutional Deep Neural Network is provided. In particular, the raw data coming from the accelerometer sensor of a smartphone are arranged to form an image including several channels (HAR-Image), which is used as fingerprints of the in-progress activity that can be used as an additional input by tracking applications. Experimental results, obtained by analyzing real data, have shown that the HAR-Images are effective features for human activity recognition. Indeed, the results on the k-fold cross-validation and obtained by using a real dataset achieved an accuracy very close to 100%.

scholarly journals Dynamic Segmentation for Physical Activity Recognition Using a Single Wearable Sensor

2021 ◽  
Vol 11 (6) ◽  
pp. 2633
Author(s):  
Nora Alhammad ◽  
Hmood AlDossari
Keyword(s):  
Physical Activity ◽  
Activity Recognition ◽  
Spinal Cord Injuries ◽  
Window Size ◽  
Sliding Window ◽  
Machine Learning Techniques ◽  
Accelerometer Sensor ◽  
Dynamic Segmentation ◽  
Physical Activity Recognition ◽  
Window Approach

Data segmentation is an essential process in activity recognition when using machine learning techniques. Previous studies on physical activity recognition have mostly relied on the sliding window approach for segmentation. However, choosing a fixed window size for multiple activities with different durations may affect recognition accuracy, especially when the activities belong to the same category (i.e., dynamic or static). This paper presents and verifies a new method for dynamic segmentation of physical activities performed during the rehabilitation of individuals with spinal cord injuries. To adaptively segment the raw data, signal characteristics are analyzed to determine the suitable type of boundaries. Then, the algorithm identifies the time boundaries to represent the start- and endpoints of each activity. To verify the method and build a predictive model, an experiment was conducted in which data were collected using a single wrist-worn accelerometer sensor. The experimental results were compared with the sliding window approach, indicating that the proposed method outperformed the sliding window approach in terms of overall accuracy, which exceeded 5%, as well as model robustness. The results also demonstrated efficient physical activity segmentation using the proposed method, resulting in high classification performance for all activities considered.

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