Suggested headings (max. 10 pages; 10pt-12pt font size in single linespacing)- Student details: name and SID- Project title (you are free to give a cool name as the project can be used for many purposes)- Introduction: description of the project. - SCRUM Sprint and Design: give description of each key component and system architecture (can follow the given diagram but can’t be exactly same). Give description of the backlog, each sprint created and weekly sprint progress chart (burndown chart). - Implementation: description of technologies and techniques used with respect to each of system components/functionalities described in the Design. - Evaluation: description of experiments and discussion of results - Discussion: Challenges, limitations and open issues. - Version Control: give screen shop of the GitHub version control log - Summary/conclusion: summary and/or concluding remarks - References including Bitbucket project repository/wiki

Testing: After training a model, you should evaluate and test the application. Classification accuracy is a simple metric to measure the performance of a trained model. In addition, confusion matrix could clearly show the performance of our model on the recognition of each activity (Testing of Machine learning models and confusion matrix will be covered in week 4 lecture notes) . The two evaluation metrics are also GIVEN in the example code. 4. Code refactoring and Version Control: The given example code reflects the state of the art engineering for IoT. Please refactor the code to make the code easily to read/understand (e.g., comments) and extensible (those techniques for design pattern and software refactoring taught in the unit). The changes shall be reflected in the GitHub version control.

. Data loading and preprocessing: In this stage, based on the workshop materials provided, you need to firstly visualize the sensor data to get some idea of the underlying human activity pattern. Based on the given codes, apply the signal filtering and visualize the cleaned data. 2. Feature engineering for sensor data: In this stage, you need to extract features from the cleaned sensor data. In the example code, min, max, and mean values of three accelerometers in the wrist sensor are extracted as features of each human activity. In this assignment, you need to focus on feature engineering (try to extract more features from more sensors based on the Week 3 lecture note, and research how different features influence the performance of human activity recognition based IoT application). Then, you could use the GIVEN code to construct training datasets. In this stage, you could train different GIVEN machine learning models based on training feature set. The code of recognition models is GIVEN, where KNN and SVM classifier are used to learn human activity recognition