Contact: Flaviana Tagliaferri
Mail: flaviana.tagliaferri@hs-mittweida.de
Application field
- Industry 4.0,
- Ambient Assisted Living,
- Safety at work,
- Healthcare
Technology
Methodological knowledge in the combination of physically differently functioning sensors and their joint data analysis, using statistical and AI methods, from pre-, in- and post-processes correlates with significant parameters regarding production technologies and quality features.
Transfer of this knowledge to the areas of human-machine interaction, especially in the area of environmental detection and vibrotactile feedback.
Activity
1) Multi-sensory machine, system and process monitoring by correlating information from pre-, in- and post-process stages with focus on the area of:
– Process assurance
– Quality monitoring
– Predictive maintenance
2) Human-machine interaction with a focus on:
– Environmental Monitoring, Decision Support, Wearable Systems
– Applications in the areas of “Health Care” and “Ambient Assisted Living”
Keywords
System and process monitoring, Human-machine interaction, Safety, Decision Support, Environment detection system, Sensors.
Operation of the proposed solution
Safety-jacket:
Accidents in the logistics and warehousing (as for production) sector are often associated with serious suffering for those affected. In this area, very serious accidents sometimes occur due to avoidable carelessness, especially with transport vehicles such as forklifts.
Existing warning systems – for example rotating lights or acoustic systems – fail. However, the cause is not the technical system, but rather human perception. The “information” from classic warning systems is often hidden due to sensory overload. People who work in a concentrated manner ignore and overlook the signals, this is commonly known as the “cocktail party effect” and “tunnel vision”.
Autonomous systems will make this problem even worse. Avoiding serious accidents through hazard detection and active warning of people at risk using combined warning systems (including in the area of autonomous driving) can offer a smart solution. For this purpose, the special features of human-machine interaction must be precisely evaluated.
The use of tactile alarm systems is a good option, since people react much more decisively and therefore more confidently to “physical contact” that immediately penetrates their personal field.
The research group has developed and is now optimizing a vibrotactile wearable device (jacket) capable of sensing incipient danger through several sensors integrated into the device. In this case, the wearable device alerts the wearer with haptic signals.
Challenges
1) Miniaturized, statistics and AI-based systems for predictive monitoring of production systems and quality monitoring
2) Miniaturized, fully automatic and stable assistance systems for the protection of people in areas with high risk potential including human-oriented feedback
Bibliography
Blümel, K., Tagliaferri, F., Kuhl, M., Algorithm for calculating distance and sensor-object angle from raw data of ultra-low power, long-range ultrasonic time-of-flight range sensors, Procedia CIRP, 2023, 118, pp. 1061–1065.
Blümel, K., Kuhl, M., Hölzel, F.A., Kunze, K., A microcontroller-based multi-sensor system using ultrasonic range sensors and radar sensors for sensor data fusion: accuracy study and performance test, Proceedings of SPIE – The International Society for Optical Engineering, 2023, 12621, 1262119
Blümel, K., Kuhl, M., A Vibrotactile Assistance System for Factory Workers for Accident Detection and Prevention in the Logistics Sector, Lecture Notes in Networks and Systems, 2023, 674 LNNS, pp. 62–71
Allmacher, C., Seiderer, A., Klimant, P., Kuhl, M., Synergy Analysis and verification of connected Cyber Physical Systems using virtual commissioning, Procedia CIRP, 2021, 99, pp. 639–644
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