# Deep locomotion prediction learning over biosensors, ambient sensors, and computer vision

## TL;DR

This paper introduces a new system that uses biosensors, ambient sensors, and computer vision to predict human movement more accurately.

## Contribution

The novel approach combines biosensors, ambient sensors, and computer vision with a modified Hidden Markov Model and a deep Exponential Residual Neural Network for locomotion prediction.

## Key findings

- The proposed system uses data from five different datasets and applies preprocessing and feature extraction techniques.
- A modified Hidden Markov Model and a deep Exponential Residual Neural Network are used for locomotion prediction.
- Experimental results show the system's effectiveness in predicting locomotion.

## Abstract

Innovative technologies for developing intelligent systems related to locomotion prediction learning are crucial in today’s world. Human locomotion involves various complex concepts that must be addressed to enable accurate prediction through learning mechanisms. Our proposed system focuses on locomotion learning through vision RGB devices, ambient sensors-based signals, and physiological motions from biosensing devices. First, the data is acquired from five different scenarios-based datasets. Then, we pre-process the data to mitigate the noise from biosensors and extract body landmarks and key points from computer vision-based signals. The data is then segmented using a data windowing technique. Various features are extracted through multiple combinations of feature extraction methodologies, followed by feature reduction using optimization techniques. In contrast to existing systems, we employ both machine learning and deep learning classifiers for locomotion prediction, utilizing a modified body-specific sensor-based Hidden Markov Model and a deep Exponential Residual Neural Network, respectively. System ontology is also presented to elucidate the relationships among the data, concepts, and objects within the system. Experimental results indicate that our proposed biosensor-based system exhibits significant potential for effective locomotion prediction learning.