Activity Recognition Using Accelerometers
Name
Hristijan Sardjoski
Abstract
Activity recognition is considered to have a wide range of applications, especially in the health sector. The assessment of different activities of daily living is useful because it can highlight information related to a specific health condition such as obesity, overweight, stroke, or fall. Moreover, the prevalence of different user-friendly wearable devices enables collecting tri-axial accelerometer data in a non-intrusive and discrete manner.
The accelerometer data used for activity recognition in this thesis is provided by SPHERE [1]. The accelerometer readings are recorded from four wearables attached on a single person's hands and legs.
This thesis compares the capabilities for activity recognition of the random forest model and the long short-term memory neural network to discern among 9 in-door activities including brushing teeth, eating a meal, flossing, getting dressed/undressed, mixing (food), spreading (food), walking, washing hands, writing. In addition, the list of activities is extended with an unknown activity. Greater focus is given on the following topics: feature extraction, segmentation of the time-series accelerometer data, parameter and hyper-parameter tuning, model training, model evaluation and generalization capability. The results suggest that the random forest model using the accelerometer-based extracted features slightly outperforms the long short-term memory neural network using raw accelerometer data when the activity recognition task is limited on the 9 chosen activities, and, additionally, when the unknown activity is included.
The accelerometer data used for activity recognition in this thesis is provided by SPHERE [1]. The accelerometer readings are recorded from four wearables attached on a single person's hands and legs.
This thesis compares the capabilities for activity recognition of the random forest model and the long short-term memory neural network to discern among 9 in-door activities including brushing teeth, eating a meal, flossing, getting dressed/undressed, mixing (food), spreading (food), walking, washing hands, writing. In addition, the list of activities is extended with an unknown activity. Greater focus is given on the following topics: feature extraction, segmentation of the time-series accelerometer data, parameter and hyper-parameter tuning, model training, model evaluation and generalization capability. The results suggest that the random forest model using the accelerometer-based extracted features slightly outperforms the long short-term memory neural network using raw accelerometer data when the activity recognition task is limited on the 9 chosen activities, and, additionally, when the unknown activity is included.
Graduation Thesis language
English
Graduation Thesis type
Master - Computer Science
Supervisor(s)
Meelis Kull
Defence year
2019