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Table 1 Summary of recent accelerometer-based step counting studies

From: Novel algorithm for a smartphone-based 6-minute walk test application: algorithm, application development, and evaluation

Study Walking conditions Sensor location Equipment (sampling rate) Step detection algorithm Goal Results
Ying (2007) [20] Treadmill Lateral side of left and right foot Dual axis accelerometers (200 Hz) Pan Tompkins, template, dual axis peak detection Accurate step detection Qualitative comparison
Zijlstra (2003) [24] Hallway Trunk Triaxial accelerometer (100 Hz) Peaks preceding sign change in forward acceleration Foot strike Within 0.02 s (SD <0.03)
Huang (2012) [10] Treadmill 5 locations HTC smartphone (10 Hz) Threshold from training period Count steps 93-96% step count Accuracy
Naqvi (2012) [11] Level ground Near centre of mass (COM) Smartphone (100 Hz) Adaptable threshold Count steps 1-2 step error (of 15-40 steps)
Kim (2004) [22] Hallway Ankle MEMS accelerometer, vertical and forward (100 Hz) Sequential thresholds to recognize swing phase, foot strike Count steps, estimate distance <1% step count error 5% distance error
Yang (2012) [3] 25 m, hallway Lower back in belt HTC smartphone (25 Hz) Peaks preceding sign change in forward acceleration, manually verified Foot strike, regularity, symmetry Visually verified to 100% accuracy
Ayub (2012) [12] Hallway 3 locations HTC smartphone (25 Hz) interpolated 50 Hz Zero crossing and threshold lengths, Variance detector Step count, stride length 1.5-5% step count error
Derawi (2010) [23] 20 m, level ground Left leg by hip Accelerometer (100 Hz) Neighborhood search for minimum peaks Cycle detection, distance metric EER = 5.7%
Martin (2011) [25] Varying speeds Varying locations Accelerometer (30 Hz) Continuous wavelet transform (CWT) Stride length (step counting) Not reported
Kim (2013) [21] Treadmill varying speeds Left waist Triaxial accelerometer (32 Hz) Heuristic, adaptive threshold, adaptive locking period Step count and activity monitoring 97% Recognition rate