Flooding Level Classification by Gait Analysis of Smartphone Sensor Data
[+] Abstract :- Urban flooding is a common problem across the world. In India, it leads to casualties every year, and financial loss to the tune of tens of billions of rupees. The damage done due to flooding can be mitigated if the locations deserving attention are known. This will enable an effective emergency response, and provide enough information for the construction of appropriate storm water drains to mitigate the effect of floods. In this work, a new technique to detect flooding level is introduced, which requires no additional equipment, and consequent installation and maintenance costs. The gait characteristics in different flooding levels have been captured by smartphone sensors, which are then used to classify flooding levels. In order to accomplish this, smartphone sensor readings have been taken by 12 volunteers in pools of different depths, and have been used to train machine learning models in a supervised manner. Support vector machines, random forests and naïve bayes models have been attempted, of which, support vector machines perform best with a classification accuracy of 99.45%. Further analysis of the most relevant features for classification agrees with our intuition of gait characteristics in different depths.
Details Regarding Dataset:
Readings were taken by different volunteers. Each volunteer was asked to walk in a straight line with mobile phone vertical, display facing volunteer in different depths of water (without making a U-turn). When he/she reached the end he/she would stop recording after coming to a stop. This is considered as a single 'reading'. Readings were sampled at 10 Hz. The dataset places readings belonging to a particular depth together in the csv file, without making a distinction regarding where each reading starts and ends. Readings belonging to same volunteer for same depth were placed continuously. In our research work, features were computed over a window of 50 continuous readings. Adjacent windows have 50% overlap. Windows of data were considered without considering the boundary between readings (except in case of different depths).
Model of smart phones used:
Samsung Galaxy S8+ and Lenovo Zuk Z2 plus were used to record the data, both of which are based on Android operating system.
Please cite our paper if you use our data:
Panchal, Ujjawal K., Hardik Ajmani, and Saad Y. Sait. "Flooding level classification by gait analysis of smartphone sensor data." Ieee Access 7 (2019): 181678-181687.
Any further questions may be addressed to the authors of this paper.
[+] Repo Information:-
1. RAW data from mobile sensors is available at data/raw/raw_data_reduced.csv
2. Pre - processing is done by code/preprocessing/transformation.py
3. Pre - preprocessed data is then stored at data/transformed/preprocessed_data.csv
4. Windowed data generation is done at code/window analysis/windowed_data_generation.py
5. Windowed data is available at data/windowed/window_50_stride_25.csv
6. Models are available in code/models
5. Old contains the old folders and unused files.
6. Saad contains Saad sir's scripts.