I am reading about applications of clustering in human motion analysis. I started out with random numbers and applied k-means clustering algorithm but I wanted to have some graphs that circle the clusters as shown in the picture. Basically, the lines represent the motion trajectory. I will appreciate ideas on how to obtain motion trajectory of a person. Application is patient monitoring where the trajectory will be used in abnormal behavior activity.
I will be using a kinect and recording the motion trajectory based on skeleton tracking. So, I will be recording the 4 quaternion values of Head, Shoulder and Torso joints and the RGBD (Red green blue Depth) that is combined as 1 value for these joints. So, a total of 4*3 + 3 = 15 time series. So, there are 15 variables. How do I convert them to represent the trajectories shown below and then apply clustering to cluster trajectories. The clusters will allow in classification.
Can somebody please show how to obtain the diagram similar to the one attached? and how do I fuse and convert the 15 time series from each person into a single trajectory.
The picture illustrates the number of clusters that are generated for the time series. Thank you in advance.
K-means is a bad fit for trajectories.
It needs to be able to compute the mean (which is why it is called "k-means"). Having a stable, sensible mean is important. But how meaningful is the mean of some time series, even if you could define it (and the series weren't e.g. of different length, and different movement speed)?
Try hierarchical clustering, and multivariate dynamic time warping.
Related
I have a greyscale image, represented by a histogram below (x and y axes are pixels, z axis is pixel intensity).
Each cluster of bars represents an object, with the local maxima fairly approximating the centroid of the object. My goal is to find the Full Width Half Max of each object – so I'm roughly approximating each object as a Gaussian distribution.
How can I detect each cluster individually? I understand how to mathematically calculate the FWHM, but I'm not sure how to detect each cluster based on its (roughly) Gaussian features. (e.g., in the example below I would want to detect 6 clusters. One can see a small cluster in the middle but its amplitude is so small that I am okay with missing it).
I appreciate any advice - and efficiency is not a major issue, so I can implement relatively expensive solutions.
To find the centers of each of these groupings you could use a type of A* search algorithm, or similar linear optimization algorithm.
It will find its way to the maxima of a grouping. The issue after that is you wont know if you are at a local maxima (which in your scenario is likely). After your current search has bottomed out at the highest point, and you have calculated the FWHM for that area, you could set all the nodes your A* has traversed to 0, (or mark each node as visited so as to not be visited again), and start the A* algorithm again, until all nodes have been seen, and all groupings found.
I have ten measurements, containing 4 signals, with a length of 6-8 hours each at a sample rate of 10Hz. (200k-300k samples per measurement)
3 signals are the x-, y- and z-axis of an accelerometer (measuring the acceleration m/s^2). This sensor is positioned on a sphere. The sphere is inflated and deflated. The air-fluctuation in the sphere is the 4th signal.
The accelerometer values, are thus a representation of fluctuation of air in the sphere. If the sensor is positioned on top of the sphere, a good correlation is seen between air fluctuation and Z-axis of the accelerometer. The X- and Y- axis should in a perfect situation be 0, but they are not and contain noise.
I have tried many methods of combining the three signals into one, and comparing that to the air-fluctuation signal. At some points this does give a good outcomes, but when the accelerometer is not moving over one axis, the signal-noise ratio seems to be too high (e.g. when the sensor is at a 45 degree angle).
I am wondering if computer machine learning in Matlab can be used here, to automatically generate a model that makes the best fit: combine the 3 axis signals into one, that best represents the 4th signal. I assume this is what computer machine learning is about?
I can provide the signals in filtered format, the integrated signals, the angle of the sensor at a given time, full signal or just a minute of data, etc.
I have however no idea how to start and which toolbox to use to tackle this problem. Also what signals to feed into the algorithm? at what length (full vs couple of seconds/minutes)
Can you help me getting started on the computer machine learning process, to use 3 signals (and/or formatted versions of them) into one signal that closely matches the fourth signal*.
I'm not asking for full code, but what steps to take to tackle this problem. Like have a look at that toolbox, and function x.
I have certain movement data acquired from motion capture system which I want to automatically choose which 5 signals are more alike.
Picture shows example of the particular data, all normalized to 100 samples due to the difference in speed.
Data set for knee flexion/extension
What I am looking for is some idea to actually compare the shapes of the curves.
The easiest solution is just to substract the "raw" curves and check which one has the smallest RMSE.
But looking at your data (which are smooth curves), another option is to use PLS or GMM to describe them. Then you can use RMSE to compute the error between your input curve and your database of curves and take the one with lowest error.
I'm currently working on a project where I should classify hand gestures, many papers proposed that HMMs is the way to do so, many tutorials speak of either a weather tutorial or a dice and coin tutorial, I can't seem to understand how to map these to my problem and what should my different matrices be, I currently have a feature vector (containing the detected features of the hands as a n*2 matrix where n is the total number of features detected in all the frames, i.e. if the algorithm detected 10 features in each frame and the video is 10 frames, n would be = 100, and 2 is the x and y coordinates) and the motion vector (the motion of the hand itself in the video m*2 size where m is the number of frames in the video) also any other data u would recommend to extract from the video.
I know the papers you are talking about and the exemples about the weather are simplistic and cannot be mapped to most of the problems now processed with HMMs. In your case, you have features corresponding to hand gestures that you know. HMM can work because the data you have is dynamic, i.e. ordered in time.
My advice is that you should first have a look at the widely used HMM toolbox by Kevin Murphy. It provides all the tools you need to start working with HMMs.
The main idea is to model each gesture type with one dedicated HMM. For a given gesture type, the corresponding HMM will be trained with the available features that you have.
Once trained, you get a state transition probability matrix, an emission probability matrix and a prior for selecting the initial state.
When your have an unknown gesture, you will then compute the likelihood this gesture (its features actually) could have been generated by each of the trained HMMs. Usually, the query sequence is assigned to the category of the one raising the highest score.
This is for the big picture. In your case, you will have to find a way to represent your features as a time series. The "time" being the different frames. With a complex application such as hand gesture it might be difficult to see what each state of the model represents. Some kinds of HMM, by their topology (left-to-right models for instance) make this analogy easier.
i'm trying to write the code of this article:"Improving Cluster Selection and Event Modeling in Unsupervised Mining for Automatic Audiovisual Video Structuring"
a part of the it is about video clustering:"The video stream is segmented into shots based on color histograms to detect abrupt changes and progressive transitions. Each of the resulting shot is summarized by a key frame, taken in the middle of the shot, in turn represented as a RGB histogram with 8 bins per color. Bottom-up clustering relies on the Euclidean distance between the 512-dimension color histograms using Ward’s linkage."
i've done this and reached to an array of numbers like this: 1.0e+03 *
3.8334
3.9707
3.8887
2.1713
2.5616
2.3764
2.4533
that after performing the dendrogram part, the result became:
174.0103
175.0093
176.0093
177.0093
178.0093
178.0093
179.0093
but according to a toy example that was given by authors of the article the result should be intervals like:
{47000, 50000}, {143400, 146400}, {185320, 187880},{228240, 231240}, {249440, 252000}, {346000, 349000}
what is wrong here?
You should have 512 dimensional vectors at the first step, one such vector per frame, or equivalently a 512 x n matrix.
Then in the second step I don't think they use the plain built-in hierarchical clustering - which is not time aware, and will not produce intervals, plus it will scale O(n^3) which is really bad - but instead they use a customized clustering algorithm, inspired by hierarchical clustering and using Ward's linkage, but which operates on time intervals; starting with single frames, but only joining neighboring intervals, not arbitrary intervals like regular hierarchical clustering would.