No abstract

No abstract

State-of-the-art in-home activity recognition schemes with wearable devices are mostly capable of detecting coarsegrained activities (e.g., sitting, standing, walking, or lying down), but are not able to distinguish complex activities (e.g., sitting on floor vs. sofa vs. bed). Such schemes are often not 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, contextual and fine-grained recognition of complex daily activities of users. In this work, we propose a novel approach for in-home, fine-grained activity recognition with the help of multi-modal wearable sensors on multiple body positions of the users and lightly deployed Bluetooth beacons in the environment. In particular, our solution exploits measuring user's ambient environment and location context with wearable sensing and Bluetooth beacons, along with user movement captured with accelerometer and gyroscope sensors. The proposed algorithm is a two-level supervised classifier with both level running on server. In the first level, multi-sensor data from wearable on each body position are collected and analyzed using our proposed modified Conditional Random Field (CRF) based supervised activity classifier. The classified activity state from each of the wearables data are then fused for deciding the final activity state of user. Preliminary experimental results are presented on the classification of 19 complex daily activities of a user at home.

Data collection from deployed sensor networks can be with static sink, ground-based mobile sink, or Unmanned Aerial Vehicle (UAV) based mobile aerial data collector. Considering the large-scale sensor networks and peculiarity of the deployed environments, aerial data collection based on controllable UAV has more advantages. In this paper, we have designed a basic framework for aerial data collection, which includes the following five components: deployment of networks, nodes positioning, anchor points searching, fast path planning for UAV, and data collection from network. We have identified the key challenges in each of them and have proposed efficient solutions. This includes proposal of a Fast Path Planning with Rules (FPPWR) algorithm based on grid division, to increase the efficiency of path planning, while guaranteeing the length of the path to be relatively short. We have designed and implemented a simulation platform for aerial data collection from sensor networks and have validated performance efficiency of the proposed framework based on the following parameters: time consumption of the aerial data collection, flight path distance, and volume of collected data.

No abstract

Abstract-In this paper we have proposed and designed FindingHuMo (Finding Human Motion), a real-time user tracking system for Smart Environments. FindingHuMo can perform device-free tracking of multiple (unknown and variable number of) users in the Hallway Environments, just from non-invasive and anonymous (not user specific) binary motion sensor data stream. The significance of our designed system are as follows: (a) fast tracking of individual targets from binary motion datastream from a static wireless sensor network in the infrastructure. This needs to resolve unreliable node sequences, system noise and path ambiguity; (b) Scaling for multi-user tracking where user motion trajectories may crossover with each other in all possible ways. This needs to resolve path ambiguity to isolate overlapping trajectories; FindingHumo applies the following techniques on the collected motion datastream: (i) a proposed motion data driven adaptive order Hidden Markov Model with Viterbi decoding (called Adaptive-HMM), and then (ii) an innovative path disambiguation algorithm (called CPDA). Using this methodology the system accurately detects and isolates motion trajectories of individual users. The system performance is illustrated with results from real-time system deployment experience in a Smart Environment.

No abstract

No abstract