Accurate quantification of steps from multiple smartphone positions.

A key challenge in smartphone-based assessment of motor capacity is that patients may wear their smartphone in varying positions, while state-of-the-art algorithms have not been designed and validated for multiple device locations. This paper proposes a solution to estimating foot-to-ground initial contacts (ICs) during gait using inertial measurement unit (IMU) sensor data collected from a smartphone agnostic of its location in a cloth front or back pocket. FAIR-Q, an algorithm originally validated for data collected from the lower trunk was further tuned for this intended use, and tested on IMU data collected in cloth pocket positions from both healthy adults (n = 83, age range: 20–83 y.o.) and people with Multiple Sclerosis (n = 50, age range: 22–61 y.o., EDSS score: 0–6) during a 30s walk test. The performance of FAIR-Q was compared against a gold standard multi-sensor device in terms of sensitivity and measurement error in identifying ICs and measuring step and stride durations. Excellent performance was achieved for both groups in all tested conditions (test-level relative errors for duration measures < 1%) and using data from a large variety of smartphone devices, supporting the method’s suitability for high-frequency smartphone-based assessment of gait capacity in neurological diseases.