A system for activity recognition using multi-sensor fusion

Human Activity Recognition from Body Sensor Data using Deep Learning

In recent years, human activity recognition from body sensor data or wearable sensor data has become a considerable research attention from academia and health industry. This research can be useful for various e-health applications such as monitoring elderly and physical impaired people at Smart home to improve their rehabilitation processes. However, it is not easy to accurately and automatically recognize physical human activity through wearable sensors due to the complexity and variety of body activities. In this paper, we address the human activity recognition problem as a classification problem using wearable body sensor data. In particular, we propose to utilize a Deep Belief Network (DBN) model for successful human activity recognition. First, we extract the important initial features from the raw body sensor data. Then, a kernel principal component analysis (KPCA) and linear discriminant analysis (LDA) are performed to further process the features and make them more robust to be useful for fast activity recognition. Finally, the DBN is trained by these features. Various experiments were performed on a real-world wearable sensor dataset to verify the effectiveness of the deep learning algorithm. The results show that the proposed DBN outperformed other algorithms and achieves satisfactory activity recognition performance.

Activity Recognition for Medical Teamwork Based on Passive RFID

We describe a novel and practical activity recognition system for dynamic and complex medical settings using only passive RFID technology. Our activity recognition approach is based on the use of objects that are specific for a given activity. The object-use status is detected from RFID data and the activities are predicted from the statuses of use of different objects. We tagged 10 objects in a trauma room of an emergency department and recorded RFID data for 10 actual trauma resuscitations. More than 20,000 seconds of data were collected and used for analysis. The system achieved a 96% overall accuracy with a 0.74 F-score for detecting use of 10 common resuscitation objects and 95% accuracy with a 0.30 F Score for activity recognition of 10 medical activities.

Dealing with the effects of sensor displacement in wearable activity recognition

Most wearable activity recognition systems assume a predefined sensor deployment that remains unchanged during runtime. However, this assumption does not reflect real-life conditions. During the normal use of such systems, users may place the sensors in a position different from the predefined sensor placement. Also, sensors may move from their original location to a different one, due to a loose attachment. Activity recognition systems trained on activity patterns characteristic of a given sensor deployment may likely fail due to sensor displacements. In this work, we innovatively explore the effects of sensor displacement induced by both the intentional misplacement of sensors and self-placement by the user. The effects of sensor displacement are analyzed for standard activity recognition techniques, as well as for an alternate robust sensor fusion method proposed in a previous work. While classical recognition models show little tolerance to sensor displacement, the proposed method is proven to have notable capabilities to assimilate the changes introduced in the sensor position due to self-placement and provides considerable improvements for large misplacements.