DevMesh

IoT based Worker Activity Assessment Wearable

Shriram KV

Shriram KV

Bengaluru, Karnataka

7 0

Many industries claim that loss incurred to them is mainly due to the poor performance of the workers. Surveys reveal that disengaged workers are contributing $400 to $500 billion in lost revenue of the companies. ( Reference: http://boldculture.co/disengaged-workers-contributing-400-500-billion-lost-revenue/ ) It is reported that derelict workers contribute to 60% of the errors in the workspaces and 49% of the accidents. IMU systems available in the market cannot detect proxies, are not foolproof and renders poor performance analysis of the worker. A foolproof IMU system which also is affordable is the ambition. ...learn more

Project status: Under Development

Internet of Things

Intel Technologies
Other

Code Samples [1]

Overview / Usage

Motivation:

Many industries claim that loss incurred to them is mainly due to the poor performance of the workers.
Surveys reveal that disengaged workers are contributing $400 to $500 billion in lost revenue of the companies. ( Reference: http://boldculture.co/disengaged-workers-contributing-400-500-billion-lost-revenue/ )
It is reported that derelict workers contribute to 60% of the errors in the workspaces and 49% of the accidents. IMU systems available in the market cannot detect proxies, are not foolproof and renders poor performance analysis of the worker. A foolproof IMU system which also is affordable is the ambition.

Technologies Involved:
IoT, Sensors, Python, Cloud and Data Analytics.

Listing down the short-comings with the IMUs in the market:

  1. It will not track if the workers are working really near the machinery.
  2. It will not track if the workers are wearing it or keeping the IMU wearable on any machinery.
  3. No IMU cares about the health of the employee. We monitor the health status of the employee through the heartbeat and temperature and alert in case of abnormalty.
  4. IMU with APP is provided here and shall enable supervisors to see the performance summary.

Methodology / Approach

We detect the body motions of the workers similar to the traditional smart bands which use accelerometer and gyro sensors.

In industries the worker always stays in the magnetic field generated by the huge machinery. By detecting the intensity of the magnetic field we can detect the presence of worker in workspace.

In order to detect the presence of band on the body, we use heartbeat and temperature sensors which cannot be generated by any other sources except by a human.

If the body parameters like heartbeat, temperature remains constant for a particular period of time then we can conclude that both the bands are worn by the same person.

Machine learning algorithms and sensor data in the band are used for deciding the effective time the workman is working in the workspace.

Depending on the heartbeat and body temperature sensor data we can predict the health of the worker. If the workman is working for a long time break reminders can be given in order to reduce the Stress levels in them.

A worker can be rated according to the effective time he had worked per month and can be given bonus in case of high rating.

In many Industries, separate working units are present which can be rated according to the worktime of the workers. Workers from such high rated units can be shifted to low rated ones for equality in Industrial performance.

Technologies Used

IoT, Sensors, Python, Cloud and Data Analytics.

Repository

https://youtu.be/ebrRrPNV_Mo

Collaborators

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