Equipment Details

ALEIS: Assisted Living Embedded Infrastructure

  ALEIS is an embedded infrastucture targeted at Assisted Living Environments. ALEIS uses RT-Chain and has been implemented on MICAz. It provides localization, fall detection as well as voice communication. In this work, ALEIS demonstrates how a sensor network enhanced with real-time communication can provide a reliable, affordable, and non-intrusive solution for real-time monitoring of Assisted Living facilities.

  Using RT-Chain, ALEIS supports real-time monitoring and emergency response. To validate its reliability, ALEIS has been deployed for testing and demonstration in Siebel Center. During the test, ALEIS was kept running for about 96 hours. This experiment validated ALEIS's reliability and scalability. In addition, ALEIS was deployed in an assisted living facility during a pilot study (see Assisted Living project for more details).

Overview of Assisted Living Embedded Infrastructure

  We propose Assisted Living Embedded Infrastructure (ALEIS) to meet the goals of building an Assisted Living Environment that is reliable, non-intrusive, extensible and affordable. ALEIS allows the elderly to live in a safer environment with peace of mind by ensuring that their safety is monitored. This infrastructure consists of the Monitoring System and the embedded hardware, MICAz Motes. The Monitoring System is a software residing in a Personal Computer in the assisted living environment itself. It can also be remotely accessed by healthcare provider centers via the Internet. The Motes are further divided into two types: Infrastructure Mote (IM) and Personal Monitor (PM). IMs are generally static after being placed, while a PM is carried by an elderly. ALEIS has been implemented as an extensible infrastructure that allows multiple different applications of varying nature to be implemented on it.

  Currently ALEIS provides real-time emergency notification, voice communication, approximate localization and fall detection. The following is a possible example scenario of how these applications are useful: suppose an elderly wears the Personal Monitor and is currently sitting in his room. The Monitoring System will reflect his location using a layout. When he stands up, he feels dizzy due to postural hypotension and falls down. The Personal Monitor will sound a beep and send an Emergency Notification to a base station. The Monitoring System will record the event, and a supervising nurse can then choose to open a voice channel to allow the elderly to request for help or simply to report that he is safe.

  We now provide more details about the three embedded applications developed on the MICAz motes as well as the server side software that acts as a control interface for users (or healthcare providers).

  • Voice over RT-Chain (VoRT)
    Upon detecting a fall, the PersonalMonitor will send an Emergency Notification packet to the base station. The base station can then choose to open a VoRT to allow the patient to request for help or notify that the detected event was a false alarm. The PM microphone is sampled at 3.33 kHz. Although the recommended sampling rate for human voice is at least 4 kHz, the received voice is still recognizable. Currently, we are not able to sample at higher frequency due to MICAz hardware limitation.
  • FallDetector
    The implemented FallDetector follows a simple algorithm and relies on MICAz accelerometer. The FallDetector always buffers a one second window of the wearer's orientation. If an impact (acceleration of 1.8 G) is detected, FallDetector will wait for further impacts within 2 seconds. After 2 seconds with no extra impacts, the FallDetector will compare the orientation of 1 second before the first impact occurred and the current orientation. A fall is detected if the change in orientation is higher than 47 degrees. A smarter fall detection mechanism has been proposed by utilizing Bayesian Network concepts. This proposal is detailed in this thesis.
  • Localization
    ALEIS also performs localization based on RSSI and the deployed Mote Infrastructure. A Personal Monitor broadcasts a localization beacon every 5 seconds, and each Infrastructure Mote that receives it will stamp the RSSI and send the packet upstream to the base station. The MonitoringSystem will then track the location of the PersonalMonitor. The implemented localization strategy simply associates the Personal Monitor to the Infrastructure Mote with the highest RSSI.
  • MonitoringSystem
    The MonitoringSystem is a software residing at the Assisted Living Environment. It continuosly collects data like PM location and emergency notification. The MonitoringSystem can also be controlled remotely by agents from a healthcare provider.


  A set of 11 motes was deployed during a testing session that lasted 96 hours. The tested scenario included two Base Motes, eight Infrastructure Motes and two Personal Monitors (PM). The main objective of this testbed was to test the system's reliability and effectiveness. Screenshots and photos of the deployment is shown below.


The green circles represent Infrastructure Motes while the blue circles represent roaming Personal Monitors.

One Infrastructure Mote and one Personal Monitor in an office environment.

Multiple Infrastructure Motes along a corridor.


   The developed software can be found in download section.


   Principal Investigator:
     Marco Caccamo (website, email)
     Chin F. Cheah
     Bach Duy Bui
     Andrew Tzakis
     Rodolfo Pellizzoni


   This work is supported in part by the NSF grants CNS-0509268 and CNS-0237884.

Copyright 2007 University of Illinois - Questions? email:
Last Updated: 5.10.07