IV. SENSOR NETWORKS FOR HUMAN
ACTIVITY MONITORING
The architecture and the platform of the sensor networks
of the HAM system play a significant role for continuous
monitoring of physiological parameters especially of the
elderly or chronic patient. The network should be selected
based on cost, performance, ease of configuration, addition
of extra sensor nodes, range and power consumption etc.
A comparison of different IEEE protocols currently available
is shown in Table I [40]. There may be different ad-hoc
networks on which research are currently undergoing.
A healthcare monitoring architecture combination of three
network tiers providing pervasive, secure access to wearable
sensor systems has been reported [41] in which the design
of the wireless sensor motes involve a Bluetooth chip with
enhanced security schemes. A few mobile health technologies
including wearable sensors for electrodermal activity (EDA)
and mobile plethysmography as well as mobile phones
and the supporting wireless network architecture has been
presented [42]. The development of a fall detection system
based on a combination of sensor networks and home robots
has been presented [43]. The sensor network architecture
comprises of body worn sensors and ambient sensors distributed
in the environment. The software architecture and
conceptual design home robotic platform along with the performance
of the sensor network in terms of latencies and
battery lifetime are discussed. A mobile platform consists of
a wearable sensor system for collecting algorithm training
data in the lab, and a mobile phone application used to
deliver therapeutic interventions as triggered by real-time
sensor data for cognitive behavioral therapy (CBT) developed
for an ongoing study for patients with drug-addiction and
post-traumatic stress disorder (PTSD) has been presented [44].
A low-cost, low-power wireless sensor platform implemented
using the IEEE 802.15.4 wireless standard, and describing the
design of compact wearable sensors for long-term measurement
of electrodermal activity, temperature, motor activity, and
photoplethysmography has been described [45].
A Body Area Network based on wireless sensors has
been optimized in for long-term recording and analysis of
walking and running gaits under extreme conditions [46].