The system was implemented in the same platform. The collected activity signals were preprocessed before experiment. The processed data was used to train and test the system in MATLAB.

• Human Activity Detection From Rgbd Images

[] [] People jysung at cs.cornell.edu hema at cs.cornell.edu selman at cs.cornell.edu asaxena at cs.cornell.edu Videos Related Projects.

In Recognize.m File You can see the Type = predict(md1,Z); so obviously TYPE is the variable you have to look for obtaining the confusion matrix among the 8 class. And unfortunately when i run the code 'Running' is the only action which has been recognized with an accuracy of 50%. All the other actions are mostly miss-classified as Surfing. And i hope this is because of extracting the features on frame level since i can see the test variable is an 50x1 dimension varaible, which is to my understanding that only 50 frames are considered for classification during the training.but this code worked as a little dynamite to my study over action recognition.

These files accompany the 'Machine Learning Made Easy' webinar which can be viewed here: About the webinar: Machine learning is ubiquitous. From medical diagnosis, speech, and handwriting recognition to automated trading and movie recommendations, machine learning techniques are being used to make critical business and life decisions every moment of the day. Each machine learning problem is unique, so it can be challenging to manage raw data, identify key features that impact your model, train multiple models, and perform model assessments. In this session we explore the fundamentals of machine learning using MATLAB ®. Dear Shashank Prasanna, You have done very good tutorial.

This code is released as part of the Human Activity Detection project at Personal Robotics Lab at Cornell University. Currently two codes are available: - Extracting features from sequence of human activity: Refer to [Sung et al. ICRA 2012] below.

Decreasing the window stride can increase the detection accuracy. However, doing so increases computation time. Increasing the window stride beyond [8 8] can lead to a greater number of missed detections. This property is tunable. Merge detection control, specified as true or false. This property controls whether similar detections are merged.

Total shipment of smartphones in 2013 was one billion units [8]. Smartphones come with a number of useful sensors including accelerometers, gyroscopes, and magnetometers. The motion related sensors, such as accelerometer and gyroscope have been widely used in activity recognition systems as a wearable sensor.

Use the same method of feature extraction that I have done here. @ Leek: Only 6 videos from the link mentioned. For others train it yourself. For detailed explanation of feature set go thro youtube links mentioned in one of the below comments Thanks to all for the ratings.

Which in turn shows how many frames are classified correctly from 50 input frames. In Case if u need more accuracy over actions you train the classifier with more input data/ Clip level data with a little change of the code provided here. Thanks Manu, It would be very helpful if u perform clip based classification with different appearance and motion features like MBH/HOF, it would be a noble contribution. Great work Bro!!!

In AAAI workshop on Pattern, Activity and Intent Recognition (PAIR), 2011. [] [] People jysung at cs.cornell.edu hema at cs.cornell.edu selman at cs.cornell.edu asaxena at cs.cornell.edu Videos Related Projects.

Human Activity Recognition using Smartphone Accelerometer Data This repository works on Smartphone Accelerometer data using the UCI ML repository data (dataset ). Every motion can be classified into a set of 6 actions: • Walking • Walking Upstairs • Walking Downstairs • Sitting • Standing • Laying We use a Machine Learning approach to solve this classification problem on streams of data. By using a stacked-autoencoder based classification method, we have created a classification schema that is agnostic to the context of the classification problem at hand. Further description of the dataset can be found. Stage 1: Windowing the data Before the training step of the process, we window the training samples and have a certain degree of samples overlapping across window borders.

I really approcciate, If you could help us with following error: Reference to non-existent field 'ClassNames'. Error in CarFinderLive>figureSetup (line 54) bar(ax2,zeros(1,numel(trainedClassifier.ClassNames)),'FaceColor',[0.2 0.6 0.8]) Error in CarFinderLive (line 6) [fig, ax1, ax2] = figureSetup(trainedClassifier); Error in CarIdentification (line 40) CarFinderLive(trainedClassifier,bag) If someone had the same problem and solved it,could you please share your experience. Thanks in advance! Keep getting an error that the ClassNames cant be found in the CarFinderLive. Bar(ax2,zeros(1,numel(trainedClassifier.ClassNames)),'FaceColor',[0.2 0.6 0.8]) Even when this is corrected with a specific path to the ClassNames Such as trainedClassifier.ClassifierSVM.ClassNames, Then a new error occurs called out by 'predict'. Where it points to line 12 if I remember correct, which states 'throw(E)'.

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Thanks Manu, It would be very helpful if u perform clip based classification with different appearance and motion features like MBH/HOF, it would be a noble contribution. Great work Bro!!! Thanks for sharing code. That helpful for my work!

The code works on all the videos in the KTH dataset, the final pop-up doesnot appear since the classifiaction results are so sparse. But still you can get the recognition accuracy of the test video in the Type(50*1) variable. Which in turn shows how many frames are classified correctly from 50 input frames. In Case if u need more accuracy over actions you train the classifier with more input data/ Clip level data with a little change of the code provided here. Thanks Manu, It would be very helpful if u perform clip based classification with different appearance and motion features like MBH/HOF, it would be a noble contribution. Great work Bro!!!

Human Activity Detection From Rgbd Images

The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. That said, I will use the almost raw data: only the gravity effect has been filtered out of the accelerometer as a preprocessing step for another 3D feature as an input to help learning. What is an RNN? As explained in, an RNN takes many input vectors to process them and output other vectors. It can be roughly pictured like in the image below, imagining each rectangle has a vectorial depth and other special hidden quirks in the image below. In our case, the 'many to one' architecture is used: we accept time series of feature vectors (one vector per time step) to convert them to a probability vector at the output for classification.

They ranged in age from 24 to 35 years. Each of the participants performed 6 activities in an uncontrolled environment. Participants placed the smartphone in the front pocket of their trouser (smartphone front faced upside down) and performed 5 full body motor activities. Each activity was performed for 2 minutes. Therefore, in total 60 minutes of sensor data for six different activities. An Android smartphone, Nexus 5, has been used in the data collection procedure. Data Analysis Figure 2: Acceleration along y-axis vs.

Data Collection The researchers have developed a data collection tool, UbiSen ( Ubicomp Lab Sensor Application for Android) to collect sensor data. It can collect data from multiple sensors simultaneously. Sensor data for five different activities has been collected: walking, walking downstairs, walking upstairs, running, and sitting. Data has also been collected with a smartphone set on a table to simulate a stationary position. The accelerometer sensor data along y-axis for these six different activities are shown in Figure 2. Data has been collected from 5 able-died male subjects.