  Stroke can lead to the impaired motor ability of the patient's lower limbs
and hemiplegia. Accurate assessment of the lower limbs' motor ability is
important for diagnosis and rehabilitation. To digitalize such assessment so
that each test can be traced back any time and subjectivity can be avoided, we
test how dual-modality smart shoes equipped with pressure-sensitive insoles and
inertial measurement units can be used for this purpose. A 5m walking test
protocol, including the left and right turns, is designed. Data are collected
from 23 patients and 17 healthy subjects. For the lower limbs' motor ability,
the tests are observed by two physicians and assessed using the five graded
Medical Research Council scale for muscle examination. The average of two
physicians' scores for the same patient is used as the ground truth. Using the
feature set we developed, 100\% accuracy is achieved in classifying the
patients and healthy subjects. For patients' muscle strength, a mean absolute
error of 0.143 and a maximum error of 0.395 is achieved using our feature set
and the regression method, closer to the ground truth than the scores from each
physician (mean absolute error: 0.217, maximum error: 0.5). We thus validate
the possibility of using such smart shoes to objectively and accurately
evaluate the lower limbs' muscle strength of the stroke patients.
