matlab report -- 5
Budget: $30 – $250 AUD
The below soume has to solved with relevant graphs and arguments supporting the solution with a detailed academic report on it.
SUM:
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 an 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. Using this data,you are to investigate if the classes o activity and 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 performance of the clustering results obtained using the GMM,HAC and DBScan. Comment on any differences observed between the three methods, and which method is more suitable 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.
SUM:
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 an 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. Using this data,you are to investigate if the classes o activity and 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 performance of the clustering results obtained using the GMM,HAC and DBScan. Comment on any differences observed between the three methods, and which method is more suitable 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.
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MATLAB