  Gait analysis holds significant importance in monitoring daily health,
particularly among older adults. Advancements in sensor technology enable the
capture of movement in real-life environments and generate big data. Machine
learning, notably deep learning (DL), shows promise to use these big data in
gait analysis. However, the inherent black-box nature of these models poses
challenges for their clinical application. This study aims to enhance
transparency in DL-based gait classification for aged-related gait patterns
using Explainable Artificial Intelligence, such as SHAP.
  A total of 244 subjects, comprising 129 adults and 115 older adults (age>65),
were included. They performed a 3-minute walking task while accelerometers were
affixed to the lumbar segment L3. DL models, convolutional neural network (CNN)
and gated recurrent unit (GRU), were trained using 1-stride and 8-stride
accelerations, respectively, to classify adult and older adult groups. SHAP was
employed to explain the models' predictions.
  CNN achieved a satisfactory performance with an accuracy of 81.4% and an AUC
of 0.89, and GRU demonstrated promising results with an accuracy of 84.5% and
an AUC of 0.94. SHAP analysis revealed that both CNN and GRU assigned higher
SHAP values to the data from vertical and walking directions, particularly
emphasizing data around heel contact, spanning from the terminal swing to
loading response phases. Furthermore, SHAP values indicated that GRU did not
treat every stride equally.
  CNN accurately distinguished between adults and older adults based on the
characteristics of a single stride's data. GRU achieved accurate classification
by considering the relationships and subtle differences between strides. In
both models, data around heel contact emerged as most critical, suggesting
differences in acceleration and deceleration patterns during walking between
different age groups.
