OBJECTIVE: Postural control is frequently compromised after sub-concussive and concussive head trauma, and balance testing is an integral part of neuromotor assessment and management. The main objective of this paper is to develop a novel smartphone-based neuromotor assessment protocol for screening of dynamic balance decrements stemming from head trauma. APPROACH: Experiments 1 and 2 compared Android smartphone orientation detection algorithms to a biomechanics laboratory motion capture system using a pendulum (i.e. non-biological movement) and a human stepping task (i.e. biological movement). Experiment 3 examined the test-retest reliability of a stepping-in-place protocol in three different sensory conditions (eyes open, no-vision, head shake) using temporal and spatial variability metrics extracted from thigh orientation signal in a sample of healthy young adults. MAIN RESULTS: Smartphone sensors provided valid measurements of movement timing and amplitude variables. However, sensor firmware version and Android OS version significantly affected quality of measurement. High test-retest reliability was shown for the temporal and spatial variables of interest during the stepping-in-place task. SIGNIFICANCE: Collectively, these experiments show that our smartphone application is a valid and reliable way to measure leg movement characteristics (mean stride time and its variability (CV), Peak Thigh SD, Thigh ROM, and Peak Return Velocity) during dynamic balance activity, which could provide an objective way to assess neuromotor function after head trauma and in other populations with balance dysfunction.
OBJECTIVE: Postural control is frequently compromised after sub-concussive and concussive head trauma, and balance testing is an integral part of neuromotor assessment and management. The main objective of this paper is to develop a novel smartphone-based neuromotor assessment protocol for screening of dynamic balance decrements stemming from head trauma. APPROACH: Experiments 1 and 2 compared Android smartphone orientation detection algorithms to a biomechanics laboratory motion capture system using a pendulum (i.e. non-biological movement) and a human stepping task (i.e. biological movement). Experiment 3 examined the test-retest reliability of a stepping-in-place protocol in three different sensory conditions (eyes open, no-vision, head shake) using temporal and spatial variability metrics extracted from thigh orientation signal in a sample of healthy young adults. MAIN RESULTS: Smartphone sensors provided valid measurements of movement timing and amplitude variables. However, sensor firmware version and Android OS version significantly affected quality of measurement. High test-retest reliability was shown for the temporal and spatial variables of interest during the stepping-in-place task. SIGNIFICANCE: Collectively, these experiments show that our smartphone application is a valid and reliable way to measure leg movement characteristics (mean stride time and its variability (CV), Peak Thigh SD, Thigh ROM, and Peak Return Velocity) during dynamic balance activity, which could provide an objective way to assess neuromotor function after head trauma and in other populations with balance dysfunction.