This document discusses the use of hand gesture recognition for human-machine interaction through wearable sensors, highlighting its applications in areas such as natural computing and user authentication. It presents a new dataset for hand gestures specifically designed for controlling IoT devices like wheelchairs, using accelerometers for motion control and additional sensors for tracking and safety. The paper emphasizes the need for a generalized benchmark for hand gestures in this context, given the existing focused but varied applications in the field.

1. The Internet-of-Things based hand gestures using wearable sensors
for human machine interaction
Abstract:
Gesture recognition has significantly attracted the research community as it has a wide range of
practical applications such as natural human computer interaction, ubiquitous computing, sign
language, user authentication and daily activity assistance. For example, the interaction between
human and tiny mobile computers such as wearable or the Internet of Things (IoT) devices that is
infeasible to be incorporated with screens or keyboards. This leads to the user’s gestures can be
one of the effective interaction methods between human and smart devices. Moreover,
interacting with human gestures might allow the user to perform their gestures and interact with
devices in natural and unobtrusive manners. Several investigations have demonstrated the
feasibility of human hand gestures for new verification authentication scheme for mobile devices
and ambient environments, while other gestures combined with activity recognition framework
can towards for situated services to help the elderly under diversity contexts.
Hand gestures for human machine interaction using wearable sensors have more potentiality than
ambient sensing thanks to its low-cost, light weight and mostly scalability everywhere at any
time. Despite the fact of existing works on human hand gestures using wearable sensors, each
focuses on a specific application and difficult to be generalized. In addition, it still lacks an
available benchmark of hand gestures in the context of human machine interaction. This paper
introduces a new human hand gesture dataset which could be suitable for controlling wheel
chair. Internet of Things (IoT) device which is currently embedded with accelerometer and
gyroscope sensors.
The Movement of the chair can be controlled by the accelerometer sensor which given as a
gesture for forward, backward, left, right and stop. GSM/GPRS is used to locate the machine and
post the coordinates to the gateway so, that we can track the user. IR Module is used to detect
object if an object is detected wheel will be stopped automatically.

2. Software Requirements:
Operating System : Linux (Raspbian)
Software’s: - gcc compiler
Server: MySql
Hardware Component:
Power Supply : +12V, 1A and +5V Regulated Power Supply
 Raspberry Pi 3
 GSM Module
 Accelerometer
 L293D Driver Circuit
 DC Motor

3. BLOCK DIAGRAM:
Block Diagram of the System
Raspberry Pi
GSM
Module
Server
Accelerometer
U
S
B
G
P
I
O
G
P
I
O
Driver Circuit
DC Motor
Web Page
IR
Module