Project

1. * 1
KONGUNADU COLLEGE OF ENGINEERING AND TECHNOLOGY
(Autonomous)
Tholurpatti (P.O), Thottiam –T.K, Tiruchirappalli – 621 215.
Department of Biomedical Engineering
Batch No: 04
Academic Year : 2023-2024
Year/Semester: III/VI
20BM605L – Mini Project II
Design of wearable device for fall detection
Presented by
Devika S (Reg. No: 621321121004)
Geetharakchana R (Reg. No: 621321121008)
Janapriya E (Reg. No: 621321121014)
Supervisor Head of the
Department
Mr. T. Ashok, M. Tech., Mr. T. Ashok, M.
Tech.,
AsP & Head / BME AsP & Head / BME

2.  In the recent years, Health Care has grown rapidly through the
recent development in the Science and Technology.
 Development in the Wireless Sensing Technology has been great
boon to the Health care industry.
 Falls are a great cause of fear and lack of confidence and
independence among older people. Sometimes falls may
even lead to death.
 Falls of the elderly always lead to serious health issues as the
decline of their physical fitness. Fracture is the most common
injury in fall of an elderly and there is also a certain possibility to
get coma, brain trauma, and paralysis.
2
Introduction

3. Abstract
 Unobserved human falls can be dangerous and can badly affect health.
Falls can cause loss of independence and fear among the older people.
In most fall events external support is essential to avoid major
consequences.
 Thus, the ability to automatically detect these fall events could help
minimizing the response time and therefore prevents the victim from
having serious injuries.
 So minimize this problem we propose a fall detection system by
using MPU6050 sensor.
 The output will be come to mobile as a SMS.
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4. Objectives
• To provide unconditional care to the senior citizen and to the
patients has some problems which can make them faint.
• To reduce the time delay between fall event and rescue time.
• To get assistance immediately from the Relations after we fall ,
through SMS notification.
• The ultimate goal of the detector system is to realize a fall event and
manage to notify an assistant immediately.
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5. Problem Identification
 Elderly people have more fear while walking alone anywhere .
 More number of sensors are used to detect fall.
 Drunken people were fall on the road and they were hard to identify.
 Early assistance may reduce the risk of problems. Hence this Fall
detection system would be great use for the mankind.
 Cameras are distributed at limited space to offer pictures or videos
of human activities to implement fall detection algorithm.
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6. Scope of the Project
 Only one sensor is used to detect the fall of elderly people.
 It reduce the time delay between fall event and rescue time.
 It is a wearable device and it been light weight.
 Whenever we fall, the system will send the location immediately
through SMS immediately.
 System will be designed to be compact and user friendly.
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7. * 7
Literature Review
Title
Author
&
Year
Remarks
An IoT-based Fall
Detection System
using Accelerometer
and Gyroscope
S. Kim, Y. Kim,
M. Kim &
2018
This paper explores an Internet of Things
(IoT)-based fall detection system utilizing
accelerometer and gyroscope sensors. It may
provide insights into sensor-based fall
detection algorithms.
An IoT-Based Fall
Detection and
Monitoring System
M. Hussain, S.
A. Ganaie, S. K.
Bhat & 2019
This conference paper discusses an
Internet of Things (IoT)-based fall
detection and monitoring system and it
discusses a wearable sensor-based fall
detection system designed for daily life
activities of older adults.

8. * 8
Literature Review
Title
Author
&
Year
Remarks
A Fall Detection
System for the
Elderly Based on the
Wrist-Worn
Accelerometer and
Gyroscope
F. Bourke, E.
Nelson, B.
Crossan &
2019
This paper explores an Internet of Things
(IoT)-based fall detection system utilizing
accelerometer and gyroscope sensors. It may
provide insights into sensor-based fall
detection algorithms.
Smartphone-Based
Fall Detection with a
Remote Analysis
System
L. N. Catarino, J.
M. Santiago, N.
P. Rodrigues&
2017
The paper introduces a smartphone-based
fall detection system with a remote analysis
component. The paper explores a novel
approach using smartphones for fall
detection.

9. Existing Block Diagram of the System
9
ADXL335
AD8232
OLED
GPS
LM35
SW420
BUZZER
ESP8266
ATMEGA 2560
POWER SUPPLY

10. Proposed Block Diagram
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WIFI MODULE
CLOUD
MOBILE SMS
POWER SUPPLY
NODE MCU
ESP8266
MPU6050
SENSOR

11.  NodeMCU ESP8266
 MPU6050 Sensor
 3.7V Rechargeable battery
 Connecting wires
 Mobile or laptop
11
Hardware Requirements

12. Description Of Component
NodeMCU ESP8266:
 Node MCU is an open source platform.
 It utilizes the ESP8266 Wi-Fi module, facilitating rapid
prototyping and development.
 Here, in our project this microcontroller fetch the data from the
sensors and it transfer to the mobile notification.
 The supply of input voltage is given by the 3.7V Rechargeable
battery.
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13. Description Of Component
MPU6050 Sensor:
 MPU stands for “Motion processing Unit”. The MPU6050 sensor
module is used to measure acceleration, velocity, orientation,
displacement, and many other motion-related parameters.
 The MPU6050 module is small in size and has low power
consumption, high repetition, high shock tolerance, and low user
price points.
 In this project, it is used to find the vibration of the objects,
whether they have fall down or not.
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14. 3.7V Rechargeable Battery:
 The battery is used for the power supply to the circuit.
 It is eco-friendly.
Connecting Wires:
 These are electric wires which is used to connect two points in a
circuit.
 In this project, the wire connections are done by soldering.
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Description Of Component

15. Mobile or Laptop:
 The output data can be visualized in mobile or laptop.
 The notification will be send to the mobile number or Email id
about the subject status.
15
Description Of Component

16.  Embedded C
 Arduino IDE
*
16
Software Requirements

17. • In conclusion, our project aimed to explore the feasibility and
effectiveness of using MPU6050 sensor to detect the falls of a
person. MPU6050 sensors proved to be reliable in detecting both
accidental and physiological falls. The accuracy of fall detection
was significantly influenced by sensor placement and calibration.
*
17
Conclusion

18. OUTPUT
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19. References
19
1. M. K. Karlsson, H. Magnusson, T. von Schewelov, and B.E.
Rosengren, “Prevention of falls in the elderly—a review,”
Osteoporosis International, vol. 24, no. 3, pp. 747–762, 2013.
2. T. Shany, S. J. Redmond, M. R. Narayanan, and N. H.
Lovell,“Sensors-based wearable systems for monitoring of human
movement and falls,” IEEE Sensors Journal, vol. 12, no. 3, pp.
658–670, 2012.
3. M. Mubashir, L. Shao, and L. Seed, “A survey on fall detection:
principles and approaches,” Neurocomputing, vol. 100, pp. 144–
152, 2013.
4. S. Sabatelli, M. Galgani, L. Fanucci, and A. Rocchi, “A double
stage kalman filter for orientation tracking with an
integratedprocessor in 9-D IMU,” IEEE Transactions on
Instrumentation and Measurement, vol. 62, no. 3, pp. 590–598,
2013.

20. References
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5. S.-L. Hsieh, K.-R. Chen, C.-L. Yeh et al., “An unfixed-position
smartphone-based fall detection scheme,” in Proceedings of the
IEEE International Conference on Systems, Man and Cybernetics
(SMC ’14), pp. 2077–2081, San Diego, Calif, USA, 2014.
6. S. D. Bersch, C. M. J. Chislett, D. Azzi, R. Kusainov, and J.S.
Briggs, “Activity detection using frequency analysis and off the-
shelf devices: fall detection from accelerometer data,” in
Proceedings of the 5th International Conference on Pervasive
Computing Technologies for Healthcare and Workshops
(Pervasive Health ’11), pp. 362–365, Dublin, Ireland, May 2011.
7. S. Abbate, M. Avvenuti, F. Bonatesta, G. Cola, P. Corsini, and
A.Vecchio, “A smartphone-based fall detection system,”
Pervasive and Mobile Computing, vol. 8, no. 6, pp. 883–899,
2012.

21. THANK YOU
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