Realtime Online Daily Living Activity Recognition Using Head-Mounted Display

Huda, Fais Al; Tolle, Herman; Asmara, Rosa Andrie

doi:10.3991/ijim.v11i3.6469

Cited by 8 publications

(8 citation statements)

References 7 publications

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“…Two techniques were employed in the window segmentation: (1) window overlapping with a fixed size window, and (2) non-overlapping with a fixed size window. The window overlapping method greatly leads to high performance but with the disadvantage of high computation needs [33]. Given our motive to achieve high performance, therefore, we selected the window overlapping method, and we overlapped each window with the half samples (50%) of the previous window.…”

Section: Segmentationmentioning

confidence: 99%

SARM: Salah Activities Recognition Model Based on Smartphone

et al. 2019

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Alzheimer's is a chronic neurodegenerative disease that frequently occurs in many people today. It has a major effect on the routine activities of affected people. Previous advancement in smartphone sensors technology enables us to help people suffering from Alzheimer's. For people in the Muslim community, where it is mandatory to offer prayers five times a day, it may mean that they are struggling in their daily life prayers due to Alzheimer's or lack of concentration. To deal with such a problem, automated mobile sensor-based activity recognition applications can be supportive to design accurate and precise solutions with an objective to direct the Namazi (worshipper). In this paper, a Salah activities recognition model (SARM) using a mobile sensor is proposed with the aim to recognize specific activities, such as Al-Qayam (standing), Ruku (standing to bowing), and Sujud (standing to prostration). This model entails the collection of data, selection and placement of sensor, data preprocessing, segmentation, feature extraction, and classification. The proposed model will provide a stepping edge to develop an application for observing prayer. For these activities' recognition, data sets were collected from ten subjects, and six different features sets were used to get improved results. Extensive experiments were performed to test and validate the model features to train random forest (RF), K-nearest neighbor (KNN), naive Bayes (NB), and decision tree (DT). The predicted average accuracy of RF, KNN, NB, and DT was 97%, 94%, 71.6%, and 95% respectively.

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Section: Segmentationmentioning

confidence: 99%

SARM: Salah Activities Recognition Model Based on Smartphone

et al. 2019

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“…The research that evaluated the AR application with Leap Motion was also done by [4], [5], but they used the SDK alpha from the Leap Motion Corp. that the access cannot be downloaded and used by the developers anymore.…”

Section: Related Workmentioning

confidence: 99%

“…HMD is a computer display device or monitor, usually used to support the Augmented Reality (AR) and Virtual Reality (VR) technology. Some studies have conducted the implementation of HMD on a certain area, for instance, the HMD is used in the Android Application Controller using Internal Gyroscope Sensor [2] and also the HMD for activity recognition [3]. Augmented Reality (AR) is a system that is built by combining reality and virtual world.…”

Section: Introductionmentioning

confidence: 99%

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Development of Augmented Reality (AR) Based Gamelan Simulation with Leap Motion Control

Permana

Tolle

Utaminingrum

et al. 2019

Int. J. Interact. Mob. Technol.

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One of the traditional musical instrument from Indonesia is gamelan, that is now less desirable because people nowadays prefer to play modern musical instruments, or because of its expensive price. This study combines the modern technology of AR and Leap Motion and also utilizes the sophisticated smartphone to develop a gamelan simulation that is presentable to the user. There are two kinds of an experiment performed in this research. The first is Augmented Reality (AR) experiment which authenticates that the distance and the height parameters are the keys to get a 100% success in running the gamelan simulation. The distance is ranged from 45 cm to 65 cm and the height is ranged from 25 cm to 30 cm. And the second one is the usability evaluation to five users, who resolves the completion task very well, acquires the result of 86.48%.

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“…This error occurs because the head moves at various speeds. The researcher [9] uses the backpropagation neural network classification method in identifying five daily activities online using the real-time system. For the noise reduction process, researchers used the Linear Acceleration API from Android, even though they had used the API, the resulting accuracy was 95%, using a total of 2035 data.…”

Section: Introductionmentioning

confidence: 99%

Pattern Recognition of Human Activity Based on Smartphone Data Sensors Using SVM Multiclass

Alman¹,

Lawi²,

Tahir³

2019

Proceedings of the 1st International Conference on Science and Technology, ICOST 2019, 2-3 May, Makassar, Indonesia

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Mobile devices are increasingly sophisticated while smartphones continue to make the latest generation that immerses the supporting tools needed in everyday life such as cameras, GPS, Microphones, and various sensors such as light sensors, a direction sensor, acceleration sensor (i.e., accelerometer) and the gyroscope sensor. This study aims to classify human activities from the accelerometer and gyroscope sensors on a Sony z3+ smartphone. To implement our system, we collect labeled accelerometer and gyroscope data from eight users when they carry out daily activity. Every activity was recorded for 22 seconds, total data that we use every activity is 2000 data with the total amount of data is 16000 data. This data we classify using the Multiclass Support Vector Machine (SVM) method reaches 97.40% accuracy using a 70% ratio as training data and 30% as test data, the classification process takes 5 seconds to classify the data.

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Realtime Online Daily Living Activity Recognition Using Head-Mounted Display

Cited by 8 publications

References 7 publications

SARM: Salah Activities Recognition Model Based on Smartphone

SARM: Salah Activities Recognition Model Based on Smartphone

Development of Augmented Reality (AR) Based Gamelan Simulation with Leap Motion Control

Pattern Recognition of Human Activity Based on Smartphone Data Sensors Using SVM Multiclass

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