COMPUTERIZED MALLET CLASSIFICATION FOR BRACHIAL PLEXUS PALSY

1. COMPUTERIZED MALLET CLASSIFICATION FOR BRACHIAL PLEXUS PALSY Charlie Ewert, Egli Spaho, Suzanne Marchant, Vincent Crocher, Na Jin Seo Hand Rehabilitation Lab, Department of Industrial and Manufacturing Engineering INTRODUCTION • Background – Mallet Classification • assesses arm motion capability • tests patients with brachial plexus injuries • consists of a series of five tasks (Fig 1) • scores from II (poor) to IV (good) • Objective • develop a computerized version using a Kinect (Microsoft, Redmond, WA) • Significance • This computerization may: • allow patient self-evaluation • provide evaluation to out-of-access patients • reduce therapists' workload • improve the diagnosis reliability and precision Figure 1: Mallet Classification APPROACH • Development • developed using C++ software and OpenNI • program prompts the patient to perform each task one by one • Kinect obtains 3D position data of the patient's body (Fig 2) Figure 2: Computerized Mallet Classification

2. COMPUTERIZED MALLET CLASSIFICATION FOR BRACHIAL PLEXUS PALSY Charlie Ewert, Egli Spaho, Suzanne Marchant, Vincent Crocher, Na Jin Seo Hand Rehabilitation Lab, Department of Industrial and Manufacturing Engineering METHODOLOGY • Evaluation • Validation performed with 10 healthy subjects • Subjects aimed for specific scores • Program scores compared with visual scores • The program: • obtains the body positions from the Kinect • calculates joint angles and body distances • computes real-time score according to rules (Fig 3) • afterwards generates report file with scores and key values Figure 3:Mallet test scoring sheet for clinic use (left with pictures, for visual assessment); and the scoring rules used by the computerized Mallet test (right).

3. COMPUTERIZED MALLET CLASSIFICATION FOR BRACHIAL PLEXUS PALSY Charlie Ewert, Egli Spaho, Suzanne Marchant, Vincent Crocher, Na Jin Seo Hand Rehabilitation Lab, Department of Industrial and Manufacturing Engineering RESULTS CONCLUSION • The test yielded a good mean accuracy of 97% (Table 1) • The computerized Mallet Classification demonstrated the feasibility of our approach • The computerized Mallet Classification has the potential to: • Enable patient self-evaluation • Reduce therapists’ workload • Provide more detailed scoring results • Enable tele-medicine and tele-evaluation • Next step is improving accuracy DISCUSSIONS • Inaccuracy usually when: • the arm was close to the body • the arm was covering body tracking points • Future studies will investigate: • Influence of color contrast between clothing, arm, and background • Influence of Kinect location • Fine tuning of the scoring rules Table 1: Evaluation results: Test accuracy BIBLIOGRAPHY [1] Piatt, J. H. Pediatric Clinics of North America, 51(2), 421-440, 2004.