The capability of mobile phones are increasing with the development of hardware and software technology.
Especially sensors on smartphones enable to collect environmental and personal information. Thus, smartphones become the key components of ambient intelligence. Human activity recognition and transport mode detection (TMD) are the main research areas for tracking the daily activities of a person.
This study aims to introduce a novel input set for daily activities mainly for transportation modes in order to increase the detection rate. In this study, the frame-based novel input set consisting of time-domain and frequency-domain features are fed to LSTM network. Thus, the classification ratio on HTC public dataset is climbed up to 97% which is 2% more than the state-of-the-art method in the literature.