Hello there,

Since I have no previous experience in machine learning (only in software development), I was wondering if the following approach could be feasible at all or if I have to dig much deeper:

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I am currently developing a software interface (Python, running on a PC) to access sensor data that is being collected by a microcontroller and sent to the PC via USB.

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The sensors are arranged in a 64×64 matrix and are placed under a piece of foil (calling the combination of it “sensor foil”). They measure pressure created by human physical activity (e.g. a hand touching the sensor foil).

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What I want to achieve is to detect different body parts or objects placed on the foil. For example left/right hand placed flat on it, a glass being placed on it or an elbow.

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Is there an approach so high level that allows me to:

– Create a fixed set of events to be detected by the system: Left/right hand, elbow and glass placed on the sensor foil

– Feed the live raw sensor data to a machine learning system running on the PC

– Train the system with the events described above. This means performing the physical action like putting my left hand down and tell the system which of the previously defined events I just made occur

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After training is finished provide the system with the live sensor data, execute one of the defined physical actions and have the system recognize the according event along with a confidence level provided

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Is that something one of the available machine learning systems can do, considering this is a proof-of-concept project with one man behind it?

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My apologies, if this request is being formulated too broad. I will gladly take in any recommendations on reading up on the matter.

Thank you!