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1. Improved Use of Foot Force Sensors and Mobile Phone GPS
for Mobility Activity Recognition
Abstract
Activity recognition is required in various applications such as medical monitoring and
rehabilitation. Previously developed activity recognition systems utilizing tri-axial
accelerometers have provided mixed results, with subject-to-subject variability. This paper
presents an accurate activity recognition system utilizing a body worn wireless accelerometer, to
be used in the real-life application of patient monitoring. The algorithm utilizes data from a
single, waist-mounted tri-axial accelerometer to classify gait events into six daily living activities
and transitional events. The accelerometer can be worn at any location around the circumference
of the waist, thereby, reducing user training.
Existing System Proposed System
 The existing system is based on the
wired technology. The mobility of the
human detect by array of sensor.
 The user’s privacy is not protected in
previous wired or wireless sensor
network.
 There is no system to monitor leg
broken outpatient.
Disadvantages:
 There is no wireless transmission.
 Always need caregiver’s help.
 High cost and may chance to human
error.
 This paper presents a system that can
be used to monitor daily activity of a
person.
 Here we are monitoring the patient’s
fall detection or physical activities
using foot force sensor.
Advantages:
 Secured data transmission.
 Real time updated information.
 Free of complex wire.

2. Working Principle:
In this system the fall detection or detecting five different mobility activities including:
such as walking, running, sitting cycling, bus-passenger, car-passenger and car-driver are
analyzed using foot force sensor and GPS. The microcontroller uses the algorithm to encrypt the
measured data and then it is transmitted using Bluetooth wireless communication. The android
mobile receiving the mobility information. The received data is decrypted and monitored in the
blue term application, then demonstrating the excellent fall detection performance and the great
feasibility of the proposed method in daily activities.
Block Diagram:
SensorNode:
PIC 16F877A
Micro
Controller
Power supply
Foot Force
Sensor
Foot Force
SensorsFoot
Force
Foot Force
Sensors
LCD
GPS

3. Monitoring Section:
Hardware Requirements:
 PIC16F877A Microcontroller unit
 Foot force Sensor
 LCD
 Blue tooth
 GPS
Software Requirements:
 Embedded C
 Keil Compiler