matlab report -- 4
Budget: $30 – $250 AUD
Sensors such as accelerometers and gyroscopes are becoming increasingly common in wearable and mobile devices. From these signals, it is possible to detect different activities, and potentially even different people. You have been supplied with a set of data collected using the accelerometer on a smart phone that captures acceleration as people go about daily office tasks. The data has five columns as follows:
• X, which indicates acceleration in the X direction;
• Y, which indicates acceleration in the Y direction;
• Z, which indicates acceleration in the Z direction;
• ActivityID, a categorical variable indicating which of 7 activities is being performed;
• SubjectID,acategoricalvariableindicatingwhichof10subjectsthesamplecorresponds to. Usingthisdata,you are to investigate if the classes of activityand the users can be separated by clustering the three acceleration variables. In particular you are to:
1. Cluster the data using a GMM with the aim of:
(a) Separating the data into the 7 activity classes. Using the provided ground truth, evaluate the accuracy of the clustering result.
(b) Separatingthedataintothe 10 identityclasses. Usingtheprovidedgroundtruth, evaluate the accuracy of the clustering result.
(c) Separating the data 70 clusters such that each cluster corresponds to a particular individual performing a particular activity. Using the provided ground truth, evaluate the accuracy of the clustering result.
2. Repeat the three clustering tasks using HAC and DBScan, and compare the performanceoftheclusteringresultsobtainedusingtheGMM,HACandDBScan. Comment onanydifferencesobservedbetweenthethreemethods,andwhichmethodismoresuitable in this situation. Your discussion should consider not just performance, but the suitability of each approach given the information available in the task, and the model hyper-parameters that need to be set.
• X, which indicates acceleration in the X direction;
• Y, which indicates acceleration in the Y direction;
• Z, which indicates acceleration in the Z direction;
• ActivityID, a categorical variable indicating which of 7 activities is being performed;
• SubjectID,acategoricalvariableindicatingwhichof10subjectsthesamplecorresponds to. Usingthisdata,you are to investigate if the classes of activityand the users can be separated by clustering the three acceleration variables. In particular you are to:
1. Cluster the data using a GMM with the aim of:
(a) Separating the data into the 7 activity classes. Using the provided ground truth, evaluate the accuracy of the clustering result.
(b) Separatingthedataintothe 10 identityclasses. Usingtheprovidedgroundtruth, evaluate the accuracy of the clustering result.
(c) Separating the data 70 clusters such that each cluster corresponds to a particular individual performing a particular activity. Using the provided ground truth, evaluate the accuracy of the clustering result.
2. Repeat the three clustering tasks using HAC and DBScan, and compare the performanceoftheclusteringresultsobtainedusingtheGMM,HACandDBScan. Comment onanydifferencesobservedbetweenthethreemethods,andwhichmethodismoresuitable in this situation. Your discussion should consider not just performance, but the suitability of each approach given the information available in the task, and the model hyper-parameters that need to be set.