Physical human locomotion prediction using manifold regularization

Abstract

Human locomotion is an imperative topic to be conversed among researchers. Predicting the human motion using multiple techniques and algorithms has always been a motivating subject matter. For this, different methods have shown the ability of recognizing simple motion patterns. However, predicting the dynamics for complex locomotion patterns is still immature. Therefore, this article proposes unique methods including the calibration-based filter algorithm and kinematic-static patterns identification for predicting those complex activities from fused signals. Different types of signals are extracted from benchmarked datasets and pre-processed using a novel calibration-based filter for inertial signals along with a Bessel filter for physiological signals. Next, sliding overlapped windows are utilized to get motion patterns defined over time. Then, polynomial probability distribution is suggested to decide the motion patterns natures. For features extraction based kinematic-static patterns, time and probability domain features are extracted over physical action dataset (PAD) and growing old together validation (GOTOV) dataset. Further, the features are optimized using quadratic discriminant analysis and orthogonal fuzzy neighborhood discriminant analysis techniques. Manifold regularization algorithms have also been applied to assess the performance of proposed prediction system. For the physical action dataset, we achieved an accuracy rate of 82.50% for patterned signals. While, the GOTOV dataset, we achieved an accuracy rate of 81.90%. As a result, the proposed system outdid when compared to the other state-of-the-art models in literature.

Intelligent ADL Recognition via IoT-Based Multimodal Deep Learning Framework

“…CNN [ 45 ] takes both data types and gives weights along with bias to different features and classifies one activity from another. It is considered to be the most effective algorithm for recognition, retrieval, and classification.…”

A Multimodal IoT-Based Locomotion Classification System Using Features Engineering and Recursive Neural Network

“…Though the proposed study achieved good accuracy results, it did not filter the noise from raw sensor data, causing problematic outcomes. In [ 12 ], Javeed et al described a system for locomotion prediction by introducing a calibration-based filter and a pattern identification technique. Additionally, a sliding overlapping technique was used for window extraction, and two datasets were utilized to perform experiments on the system.…”

EEG-Based Performance-Driven Adaptive Automated Hazard Alerting System in Security Surveillance Support

“…In recent years, various WSDs have been developed that can be used to track workers' physical activities [39,40], locomotion [41][42][43], and health-related physiological data indicating fatigue [44], vigilance and attention [45], mental workload [11], stress [46], and emotions [47] and provide early warning signs of safety issues to construction workers to mitigate health risks and safety hazards on construction sites [48,49]. Furthermore, mental-state monitoring of workers has been used to construct adaptive joint HMC systems that include a wearable biosensor for assessing workers' psychological conditions such that the automated system can adjust its working style accordingly to facilitate human trust in automation [27,50].…”