Multimodal Wearable Sensing for Fine-Grained Activity Recognition in Healthcare

  title={Multimodal Wearable Sensing for Fine-Grained Activity Recognition in Healthcare},
  author={Debraj De and Pratool Bharti and Sajal K. Das and Sriram Chellappan},
  journal={IEEE Internet Computing},
State-of-the-art in-home activity recognition schemes with wearable devices are mostly capable of detecting coarse-grained activities (sitting, standing, walking, or lying down), but can't distinguish complex activities (sitting on the floor versus the sofa or bed). Such schemes often aren't effective for emerging critical healthcare applications -- for example, in remote monitoring of patients with Alzheimer's disease, bulimia, or anorexia -- because they require a more comprehensive… Expand
Context-based Human Activity Recognition Using Multimodal Wearable Sensors
This dissertation designed a novel approach for in-home, fine-grained activity recognition using multimodal wearable sensors on multiple body positions, along with very small Bluetooth beacons deployed in the environment and introduced Watch-Dog – a self-harm activity recognition engine, which attempts to infer selfharming activities from sensing accelerometer data using wearable sensors worn on a subject's wrist. Expand
HuMAn: Complex Activity Recognition with Multi-Modal Multi-Positional Body Sensing
Experimental results demonstrate that the HuMAn system can detect 21 complex at-home activities with high degree of accuracy, and a novel two-level structured classification algorithm that improves accuracy by leveraging sensors in multiple body positions. Expand
Detailed Human Activity Recognition using Wearable Sensor and Smartphones
The contribution of this work is to present a framework for detailing in identification for both static and dynamic activities, as well as their intense counterparts by designing an ensemble of classifiers. Expand
Data-driven Context Detection Leveraging Passively Sensed Nearables for Recognizing Complex Activities of Daily Living
A methodology to automatically detect the context using passively observable information broadcasted by devices in users’ locale is developed and a pattern extraction algorithm and probabilistic mapping between the context and activities to reduce the set of probable outcomes. Expand
Fine grained activity recognition using smart handheld
This paper proposes a fine grained ubiquitous activity recognition system that uses only smartphone accelerometer that is available in any smartphone configuration and selects minimal set of features that can precisely recognize twelve activities. Expand
A Survey on Activity Detection and Classification Using Wearable Sensors
Activity detection and classification are very important for autonomous monitoring of humans for applications, including assistive living, rehabilitation, and surveillance. Wearable sensors haveExpand
Detailed Activity Recognition with Smartphones
A detailed activity recognition system that uses smartphone accelerometer (available in almost every smartphone) thus does not need any special device to be carried by the user and introduces a new feature based on jerk to detect both detailed static activities (sit on chair) and detailed dynamic activities (brisk walk). Expand
Human Physical Activity Recognition Using Smartphone Sensors
A human physical activity recognition system based on data collected from smartphone sensors that shows good accuracy for recognizing all six activities, with especially good results obtained for walking, running, sitting, and standing. Expand
An Ensemble of Condition Based Classifiers for Device Independent Detailed Human Activity Recognition Using Smartphones †
A ubiquitous solution to detailed activity recognition through the use of smartphone sensors by forming an ensemble of the condition based classifiers to address the variance due to different hardware configuration and usage behavior in terms of where the smartphone is kept. Expand
Design of a Wearable Wireless Multi-Sensor Monitoring System and Application for Activity Recognition Using Deep Learning
ConvLSTM outperforms CNN and LSTM as far as activity recognition is concerned and is focused on new dataset generation for activity recognition. Expand


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