AbstractActivity Monitoring is a key feature of health and well-being assessment that has received immense consideration from the research community over the last few decades. In recent years, smart phones with inbuilt sensors have become popular for the purpose of activity recognition. The sensors capture a large amount of data, which contain meaningful events, in a short period of time. Hence, the capability to detect, adapt and respond to such changes performs a key role in various domains such as to identify changes in patient vital signs in a medical domain or to assist in the process of generating activity labels for the purposes of annotating real-world datasets. The sudden change in mean, variance or both may represent a change point in time series data. A change point can also be used to identify the transition from one activity to another. Change point detection is a technique to process and analyse the sensor data and identify the transition from one underlying time series generation model to another.
In this thesis, the existing Multivariate Exponentially Weighted Moving Average (MEWMA) algorithm has been used to automatically detect such change points for transitions in user activity. The MEWMA approach has the advantage that it does not require any assumptions to be made in relation to the underlying distributions to evaluate multivariate data streams and can run in an online scenario.
Following this, the genetic algorithm (GA) has been used to identify the optimal set of parameters for a MEWMA approach to change point detection. The GA optimizes different parameters of the MEWMA in an effort to find the maximum F-measure, which subsequently identifies the exact location of the change point xiv from an existing activity to a new one. Furthermore, we benchmark our approach against a similar multivariate approach, namely Multivariate Cumulative SUM (MCUSUM) to automatically detect change points in different user activities. In addition, GA and Particle Swarm Optimization (PSO) are also used to automatically identify an optimal parameter set using different parameters for MEWMA and MCUSUM, so as to maximize the objective function that is Fmeasure. The evaluation is performed using different metric measures based on real and synthetic datasets collected from accelerometer sensor.The experimental results shows that the proposed approach MEWMA outperforms than the bench mark approach MCUSUM.
Hence, the accurate change point detection in the data enable a system to identify changes in user activities and recognize and monitor good behaviour such as healthy exercise patterns based on these activities.
|Date of Award||Jan 2018|
|Supervisor||Shuai Zhang (Supervisor), Sally Mc Clean (Supervisor) & Christopher Nugent (Supervisor)|
- Change Point Detection
- Activity Detection