Coming to Grips with the Objects We Grasp:

Detecting Interactions with Efficient Wrist-Worn Sensors

Eugen Berlin, Jun Liu, Kristof van Laerhoven, Brent Schiele

Department of Computer Science,

Techniche Universität Darmstadt,

Darmstadt, Germany

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Summary:

Introduction:

This article describes a wrist worn sensor that is able to identify nearby objects with RFID tags and also the gestures performed by the user through accelerometer data. In particular this paper presents the technical challenges encountered in the development of a wrist worn device to perform this task.

System:

RFID antenna

M1-mini SkyeTek reader interface circuitry

3D accelerometer

Skin temperature sensor

Two ambient light sensors

Challenges:

Extending the range of the RFID from the wrist to the hand.

Choice of antenna to best perform.

Density of RFID tags and frequency of measurements to be able to determine which item is being picked up.

Classification of 3D accelerometer data.

The Box Test:

Used in optimizing the antenna.

Objects with RFID tags were placed in a box and then individually picked up. The Hit Rate was then calculated as the number of correct identifications divided by the overall number of attempts. This test had the advantage that it had many elements which would de present in a real world application and was therefore more representative.

Variables to optimize using the box test:

Antenna shape

Q-Value, the quality or reading range

RFID Reading Frequency

Optimizing Accelerometer Data (Inertial Sensing):

How the essence of the gestures was captured. The segmentation and classification phase were combined. The data is compacted into a series of linear segments which are then compared to members from a known set with a sliding window paradigm.

Gardening and Domestic Cleaning Scenarios:

These were chosen as test beds because of their real environments containing multiple pre tagged tools and tasks.

Discussion:

This paper presents a more practically usable implementation of an RFID/accelerometer bracelet combination for identifying objects and gestures. The various challenges encountered in the development are outlined leading to an efficient lightweight solution. A classification method for dealing with the accelerometer data was also adapted to run on the reduced resources of the mobile platform.