Performance Comparisson Human Activity Recognition using Simple Linear Method

Human activity recognition (HAR) with daily activities have become leading problems in human physical analysis. HAR with wide application in several areas of human physical analysis were increased along with several machine learning methods. This topic such as fall detection, medical rehabilitation or other smart appliance in physical analysis application has increase degree of life. Smart wearable devices with inertial sensor accelerometer and gyroscope were popular sensor for physical analysis. The previous research used this sensor with a various position in the human body part. Activities can classify in three class, static activity (SA), transition activity (TA), and dynamic activity (DA). Activity from complexity in activities can be separated in low and high complexity based on daily activity. Daily activity pattern has the same shape and patterns with gathering sensor. Dataset used in this paper have acquired from 30 volunteers. Seven basic machine learning algorithm Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, Gradient Boosted and K-Nearest Neighbor. Confusion activities were solved with a simple linear method. The purposed method Logistic Regression achieves 98% accuracy same as SVM with linear kernel, with same result hyperparameter tuning for both methods have the same accuracy. LR and SVC its better used in SA and DA without TA in each recognizing

Human activity recognition for static and dynamic activity using convolutional neural network

Evaluated activity as a detail of the human physical movement has become a leading subject for researchers. Activity recognition application is utilized in several areas, such as living, health, game, medical, rehabilitation, and other smart home system applications. An accelerometer was popular sensors to recognize the activity, as well as a gyroscope, which can be embedded in a smartphone. Signal was generated from the accelerometer as a time-series data is an actual approach like a human actifvity pattern. Motion data have acquired in 30 volunteers. Dynamic actives (walking, walking upstairs, walking downstairs) as DA and static actives (laying, standing, sitting) as SA were collected from volunteers. SA and DA it's a challenging problem with the different signal patterns, SA signals coincide between activities but with a clear threshold, otherwise the DA signal is clearly distributed but with an adjacent upper threshold. The proposed network structure achieves a significant performance with the best overall accuracy of 97%. The result indicated the ability of the model for human activity recognition purposes

Performance Comparisson Activity Recognition using Logistic Regression and Support Vector Machine

Daily activities has become leading problems in human physical analysis. Autonomous system as application in several area of human physical analysis was increase along with several machine learning methods. Fall detection, medical rehabilitation or other smart home application in physical analysis application has increase degree of life. Accelerometer and gyroscope was popular sensor for physical analysis. Several research was used these sensor with various position in human body part. Activities was separated in three class, static activity, transition activity, and dynamic activity. Basic activities has same pattern in each activity. From public HAR dataset, wich have three static activity (standing, sitting, and laying) each pattern has same shape and patterns. Dataset were used in this paper have acquire from 30 volunters. Seven basic machine learning alghoritm Logistic Regression, Support Vector Machine, Decission Tree, Random Forest, Gradien Boosted and K-Nearest Neighbor. The purposed method Logistic Regression achieves 98% accuracy same as SVM with linear kernel, with same result hyperparameter tuning for both method has same accuracy. Likewise for result where this main problems static activity was successful detected with logistic regression and SVM

Triaxial accelerometer-based human activity recognition using 1D convolution neural network

Deep learning has been instrumental for human activity recognition (HAR). In spite of its strong potential, significant challenges exist, wherein the real case, deep learning model requires a massive dataset for training. However, existing research require an improvement to classify static and dynamic activity with more significant achievement. To address such challenges, we proposed a model utilizing 1-dimensional Convolution Neural Network (CNN) to classify static and dynamic activity using public dataset. The proposed scheme in this study has been conducted (through experiments), in which the result denotes the state-of-the-art methods, obtaining better performance than others

Human activity recognition for static and dynamic activity using convolutional neural network

Evaluated activity as a detail of the human physical movement has become a leading subject for researchers. Activity recognition application is utilized in several areas, such as living, health, game, medical, rehabilitation, and other smart home system applications. An accelerometer was popular sensors to recognize the activity, as well as a gyroscope, which can be embedded in a smartphone. Signal was generated from the accelerometer as a time-series data is an actual approach like a human actifvity pattern. Motion data have acquired in 30 volunteers. Dynamic actives (walking, walking upstairs, walking downstairs) as DA and static actives (laying, standing, sitting) as SA were collected from volunteers. SA and DA it's a challenging problem with the different signal patterns, SA signals coincide between activities but with a clear threshold, otherwise the DA signal is clearly distributed but with an adjacent upper threshold. The proposed network structure achieves a significant performance with the best overall accuracy of 97%. The result indicated the ability of the model for human activity recognition purposes

Optimizing Game Performance with Dynamic Level of Detail Mesh Terrain Based on CPU Usage

In making a game, a map is an important component. In making maps, several techniques can be used, one of which uses the Procedural Content Generator (PCG) method. In making maps using PCG can apply the Perlin Noise algorithm, as a generator engine for making maps automatically. The Algorithm Perlin Noise can make a noise gradient of and store values from 1 to 0 in each pixel. This value can be utilized as the height value of a 3D map formed from a point which is then connected to a surface called a mesh. The bigger the mesh, the more detailed a map will be. However, there are obstacles in its formation, namely the burden of the processor in processing the map. The level of detail (LOD) in a mesh will affect the workload of the processor, so we need a dynamic LOD. In this study, game performance measurements were performed using the average FPS with the application of Dynamic LOD and LOD Statistics. The performance test managed to increase processor performance to the maximum extent but did not reduce the overall performance of the game. In table 3 the connectedness calculation uses the person correlation method that the connectedness between the CPU and Vertex has a value of -0.81942 which means that if the CPU goes up the vertices go down, and the value of the connectedness between the CPU and LOD is 0.92299 which means if the CPU performance goes up, the LOD will go up, this indicates The CPU is optimized to run the rendering process and can optimize processor performance