Puzzling Patterns: Assessing Neck Range of Motion Using a Mobile Puzzle Exergame.

Cervical range of motion (ROM) is a crucial aspect of assessment following a neck injury and prior to cervical rehabilitation. We explored using an exergame with a head-tracker to predict the degree of cervical ROM. Using head movement, users moved a cursor over a picture-reveal puzzle to remove tiles and reveal an underlying picture. In a within-subjects user study, we controlled mobility restriction by fitting participants with either a rigid cervical collar (severe restriction), a soft cervical collar (moderate restriction), or no collar (no restriction). We also controlled task difficulty through two levels each of number of tiles (13 × 10, 7 × 5) and gain (high, low). Selection rate by mobility restriction ranged from ≈ 30% for severe to ≈ 95% with none, and ≈ 50% for moderate. Results suggest the following ascending ranks for difficulty based on number of tiles and gain: (1) 7×5, high gain, (2) 7×5, low gain, (3) 13×10, high gain, and (4) 13×10, low gain. This ascending difficulty order is recommended for presenting the puzzles to people with cervical conditions to avoid overexertion. The collected data were also used in machine learning with a Random Forest model. Mobility restriction category (severe, moderate, none) was correctly predicted in 80.6% of 36 samples. The results are a first step in using an exergame and machine learning to automatically categorize patients according to their cervical ROM.