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John Bigham

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5 papers
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5

JBHI Journal 2020 Journal Article

Inferring Micro-Activities Using Wearable Sensing for ADL Recognition of Home-Care Patients

  • Mathangi Sridharan
  • John Bigham
  • Paul Michael Campbell
  • Chris Phillips
  • Eliane Bodanese

In this study, we propose a novel, contextbased, location-aware algorithm for identifying low-level micro-activities that can be used to derive complex activities of daily living (ADL) performed by home-care patients. This identification is achieved by gathering the location information of the target user by using a wearable beacon embedded with a magnetometer and inertial sensors. The shortcomings of beacon-signal stability and mismatch issues in magnetic-field sequences are overcome by adopting a hybrid, three-phase approach for deducing the locus of micro-activities and their associated zones in a smart home environment. The suggested approach is assessed in two different test environments, where the main intention is to map the location of a person performing an activity with pre-defined house landmarks and zones in the offline labeled database. In addition to the recognition of low-level activities, the proposed method also identifies the person's walking trajectory within the same zone or between different zones of the house. The experimental results demonstrate that it is possible to achieve centimeter-level accuracy for the recognition of micro-activities and to achieve the classification accuracy of 85% for trajectory prediction. These results are encouraging and imply that the collection of accurate low-level information for ADL recognition is possible using integration of inertial sensors, magnetic field and Bluetooth low energy (BLE) technologies from the wearable beacon without relying on other infrastructural sensors.

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IJCAI Conference 2007 Conference Paper

  • Xu Yang
  • John Bigham

This paper proposes an approach for learning call admission control (CAC) policies in a cellular network that handles several classes of traffic with different resource requirements. The performance measures in cellular networks are long term revenue, utility, call blocking rate (CBR) and handoff failure rate (CDR). Reinforcement Learning (RL) can be used to provide the optimal solution, however such method fails when the state space and action space are huge. We apply a form of NeuroEvolution (NE) algorithm to inductively learn the CAC policies, which is called CN (Call Admission Control scheme using NE). Comparing with the Q Learning based CAC scheme in the constant traffic load shows that CN can not only approximate the optimal solution very well but also optimize the CBR and CDR in a more flexibility way. Additionally the simulation results demonstrate that the proposed scheme is capable of keeping the handoff dropping rate below a prespecified value while still maintaining an acceptable CBR in the presence of smoothly varying arrival rates of traffic, in which the state space is too large for practical deployment of the other learning scheme.

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KER Journal 1999 Journal Article

Agent technology in communications systems: an overview

  • ALEX L. G. HAYZELDEN
  • John Bigham

Telecommunications infrastructures are a natural application domain for the distributed software agent paradigm. The authors clarify the potential application of software agent technology in legacy and future communications systems, and provide an overview of publicly available research on software agents used for communications management. The authors focus on the intelligent agent type of software agent, although the paper also reviews the reasons why mobile agents have made an impact in this domain. The author's objective is to describe some of the intricacies of using the software agent approach for the management of communications systems. The paper is in four main sections. The first section provides a brief introduction to software agent technology. The second section considers general problems of network management and the reasons why software agents may provide a suitable solution. The third section reviews some selected research on agents in a telecommunications management framework. The final section concludes the paper by discussing some of the problems encountered and some future directions for further research.

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