A compact setup for behavioral studies measuring limb acceleration

Abstract

Behavioral studies contribute largely to a broader understanding of human brain mechanisms and the process of learning and memory. An established method to quantify motor learning is the analysis of thumb activity. In combination with brain stimulation, the effect of various treatments on neural plasticity and motor learning can be assessed. So far, the setups for thumb abduction measurements employed consist of bulky amplifiers and digital-to-analog devices to record the data. We developed a compact hardware setup to measure acceleration data which can be integrated into a wearable, including a sensor board and a microcontroller board which can be connected to a PC via USB. Additionally, we provide two software packages including graphical user interfaces, one to communicate with the hardware and one to evaluate and process the data. This work demonstrates the construction and application of our setup at the example of thumb acceleration measurement with a custom made glove and its use for research. Using integrated circuits, the size of the measurement devices is reduced to this wearable. It is simple to construct and can be operated easily by non-technical staff.

Keywords: Acceleration; Neural plasticity; Thumb movement.

Graphical abstract

Fig. 1

Cutting pattern of the glove; The materials are listed in the bill of material table. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

(a) PCB layout of the sensor board; (b) 3D model of the sensor board; (c) PCB layout of the controller board; (d) 3D model of the controller board.

Fig. 3

(a) The sensor board connected to the microcontroller board; (b) The glove applied to a wrist with the two PCBs in the accordingly attached bags.

Fig. 4

Accelera screenshots: (a) Connecting Accelera to the microcontroller board; The numbers are labeling the available buttons: (1) open a previously stored file; (2) Save recorded data to file; (3) connect a hardware device; (4) options, the corresponding widget is shown in (d); (5) record data in continuous mode; (6) start protocol mode; (7) start external trigger mode; (8) stop continuous mode; (9) software information; (b) Data visualization of Accelera in continuous mode; (c) Protocol mode with three sections; Basic Experimental Setup: allows the user to set the timings for the protocol; Experiment Progress: Shows the progress of the current experiment block, the progress of the break and the progress of the whole experiment; Acceleration Data: shows the currently recorded curve; (d) The settings window can be used to configure the hardware and calibrate the sensor.

Fig. 5

(a) Screenshots of Viewcelera are shown. The upper picture displays data from the whole experiments and the lower plot shows a single event (e.g. #20). Using the mouse, the user can select the relevant data, marked as a green and a red dot. On the upper right, minima, maxima and mean are given for acceleration, velocity and displacement. All data is calculated for x, y and z direction and the absolute value. At the lower right corner, the events where a selection was made are listed and ready for further analysis. (b) Once the relevant data was selected in an event, it can be selected to display velocity and displacement in a new tab. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 6

(a) The plots show the acceleration curves after the sensor has been turned in each direction. The directional acceleration component is at 1 g (=9.81 m s⁻²) when the corresponding sensor axis is placed vertically. At 45°, two components are involved. (b) The data shows a single thumb bending experiment. The thumb was in idle position and after a beeping sound the thumb was bent. The legend on the right side is valid for both plots.

Fig. 7

(a) Results of an experimental protocol over six blocks is shown. Each block contains 30 events of 1000 samples. One exemplary event is highlighted in red and plotted again in (b). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 8

We tested our setup with a protocol of six blocks with each 30 recording events of 1000 samples. Between each block was a break of 60 s. The peak acceleration was determined for each event and the graphs show the mean of the peak acceleration per block. The error bars show the standard deviation. (a) In two experiments, the subjects did not receive any motivation. (b) When the subjects were motivated by the examiner, the peak acceleration increased for each block.