A Windowless Approach to Recognize Various Modes of Locomotion and Transportation

Abstract

Detecting modes of transportation through human activity recognition is important in the effective and smooth operation of smartphone applications or similar portable devices. However, the effectiveness of such tasks depends on the nature and type of data provided, and it can often become quite challenging. "SHL recognition challenge 2021" is an activity recognition challenge that aims to detect eight modes of locomotion and transportation. The dataset in this challenge was based on radio data, including GPS reception, GPS location, Wi-Fi reception, and GSM cell tower scans. The objective was to create a model that was able to recognize the modes in a user-independent manner. In this paper, our team (Team Nirban) gives an appropriate summarization of our methods and approach to the challenge. We processed the data, extracted various features from the dataset, and selected the best ones, which helped our model to be generative and user-independent. We exploited a classical machine learning based approach and achieved 93.4% accuracy and 89.6% F1 score on the training set using 10-fold crossvalidation, as well as 62.3% accuracy on the provided validation set. CCS CONCEPTS• Human-centered computing → Smartphones; • Computing methodologies → Supervised learning by classification; Classification and regression trees.

Accuracy Improvement of Vehicle Recognition by Using Smart Device Sensors

“…In Refs. [14][15][16], the best accuracy obtained was 93.4%, which was lower than our results, while also using more sensors than ours to detect mode of transportation.…”

“…Incorporating the K-nearest neighbor classifier (KNN), RF, the extra trees classifier (ET), and the XGBoost classifier (XGB), Ref. [15] also took data from various radio sensors to interpret people standing still, walking, running, biking, driving a car, taking a bus, on a train, or subway. Their RF model presented their best results, with 93.4 % accuracy.…”

“…Figures [14][15][16] show the preprocessing of the data, as it is initially input in Figure 14, and then passes through the following steps 1 and 2 respectively:…”

“…The raw dataset for each class has different lengths. As such, all of them are trimmed Figures [14][15][16] show the preprocessing of the data, as it is initially input in Figure 14, and then passes through the following steps 1 and 2 respectively:…”