The overall goal of this child-friendly gaming test, based on a commercially available, digital, 3-D sensor is to objectively assess the upper limb function for clinical trials. This method can provide key information such as how the muscle function has changed for many neuro-muscular diseases. The main advantage of this technique is that the assessment is objective and quantitative with high resolution.
The implications of how our method extends to measure the disease progression of spinal muscular atrophy because children often perform poorly in the standard clinical assessment. Though our designed test can provide insights into upper limb movement, the same technique can also apply to other motor symptoms like whole body gait and balance analysis. We first had the idea for this method when we were brainstorming the possibility of gamification in the area of assessing drug effect.
Demonstration of this method is wider because it provides meaningful insight into the dynamic of the game and it's a very fast way to understand the setup. Demonstrating the procedure will be Ulrike Bonati, the medical doctor from Basel University Hospital where the study was performed and a healthy volunteer who has also participated in the study. To begin, install the 3-D sensor drivers and the wardrobe game application to the testing computer.
Then place the computer on a table with a height of 0.5 to one meters. Place the 3-D device on the table, aligned with the middle of the computer, and adjust the angle of the 3-D sensor as needed to correctly capture the subject. Next, connect the 3-D sensor adapter to the computer, the 3-D sensor, and to the power supply using the respective cables.
Finally, place an adjustable chair about two meters away from the table. Before beginning the test, start the computer and adjust the volume and ensure the internet is connected to allow for automatic data transmission. Next, instruct the subject to sit in the chair and start the application on the computer.
Then enter the unique subject ID on the first page. Click on the start button to enter the page with the wardrobe game. The skeleton figure visible on the screen represents the body of the subject in front of a large virtual wardrobe.
Instruct the subject to wave their arms and perform other movements until the 3-D sensor captures the subject. The 3-D sensor uses infra-red to detect the human body, therefore if the room has direct or strong sunlight the skeleton figure might be distorted or even not be seen. Read the instructions displayed on the screen and adjust the subject's position until the instructions are displayed in a green font.
Click on the train button to begin the training session without recording data. Allow the subject to follow the instructions given on the screen and perform arm motions as requested by the wardrobe game. First, the subject will be asked to extend one of their arms to reach and then grasp a flickering, virtual object.
Next, the subject will be instructed to flex the same arm and touch the indicated points on their body to place the virtual object. When the subject is not able to reach or place an object due to insufficient muscle function, that object will automatically be skipped after 12 seconds by the program. Alternatively, the operator can press a button to skip the object.
Click on the start button to begin recording the subject as they perform the arm motions again. After four minutes, the game will end automatically if the subject is not able to complete the series of arm motions. After the game is completed, a spider plot indicating the subject's joint ranges will appear.
Finally, click on the end button to exit the game. The traces from nine upper body points were plotted while subjects performed arm motions as part of the wardrobe game, arm function assessment. In this figure, the spacial locations of nine body points are plotted over time for a spinal muscular atrophy patient against those of a healthy control.
In comparison, the control subject had relatively less head, neck, and torso movement then the patient. Information extracted from the raw 3-D data obtained using this methodology could be used to compare the motion of a single patient over multiple rounds of testing. Shown here is a segmented hands trace plot of elbow extension and elbow flexion phases from two rounds.
The trajectory remains consistent through both rounds with notable overreach for the three lower objects for both hands. Differences between the right and left hands of a single patient were also measured. As shown in this figure, there was no significant difference between the velocity of a single patient's right and left hands.
Information extracted from the raw 3-D data also elucidated differences between individual subjects. Obvious intra-subject differences were observed in trunk compensation movements between patient two and patient three. Notable differences were also detected in median hand velocity between patient one and patient three.
After watching this video, you should have a good understanding of how to set up and execute this wardrobe game to get objective information of arm function. Once mastered, this test can be done in five minutes if performed properly. As a follow up to this procedure, other tests can be performed in order to answer additional questions, for example, about muscle endurance.
While attempting this procedure, it's important to remember that a proper match between test design and patient's ability is key of success. After its development, this technique paved the way for researchers in the field of clinical biomarkers to explore other digital devices for measuring treatment benefit and disease progression.
