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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.

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Skeleton-based Motion Capture (MoCap) systems have been widely used in the game and film industry for mimicking complex human actions for a long time. MoCap data has also proved its effectiveness in human activity recognition tasks. However, it is a quite challenging task for smaller datasets. The lack of such data for industrial activities further adds to the difficulties. In this work, we have proposed an ensemble-based machine learning methodology that is targeted to work better on MoCap datasets. The experiments have been performed on the MoCap data given in the Bento Packaging Activity Recognition Challenge 2021. Bento is a Japanese word that resembles lunch-box. Upon processing the raw MoCap data at first, we have achieved an astonishing accuracy of 98% on 10-fold Cross-Validation and 82% on Leave-One-Out-Cross-Validation by using the proposed ensemble model.