activity recognition

To assist individuals in sports activities is one of the emerging areas of wearable applications. Among various kinds of sports, detecting tennis strokes faces unique challenges. we propose an approach to detect three tennis strokes (backhand, forehand, serve) by utilizing a smartwatch. In our method, the smartwatch is part of a wireless network in which inertial data file is transferred to a laptop where data prepossessing and classification is performed. The data file contains acceleration and angular velocity data of the 3D accelerometer and gyroscope.

A commonly used definition of spatial disorientation (SD) in aviation is "an erroneous sense of one’s position and motion relative to the plane of the earth’s surface". There exists a wide range of SD use-cases dictated by situational factors, therefore SD has been predominantly studied using reduced motion detection experimental contexts in isolation. The study of SD by use-case makes it difficult to understand general SD occurrence and thus provide viable solutions. To investigate SD in a generalized manner, a two-part Human Activity Recognition (HAR) study was performed.

With the rapid deployment of indoor Wi-Fi networks, Channel State Information (CSI) has been used for device-free occupant activity recognition. However, various environmental factors interfere with the stable propagation of Wi-Fi signals indoors, which causes temporal variation of CSI data. In this study, we investigated temporal CSI variation in a real-world housing environment and its impact on learning-based occupant activity recognition.

Opportunity++ is a precisely annotated dataset designed to support AI and machine learning research focused on the multimodal perception and learning of human activities (e.g. short actions, gestures, modes of locomotion, higher-level behavior).