  Convolutional neural network (CNN) has been widely exploited for simultaneous
and proportional myoelectric control due to its capability of deriving
informative, representative and transferable features from surface
electromyography (sEMG). However, muscle contractions have strong temporal
dependencies but conventional CNN can only exploit spatial correlations.
Considering that long short-term memory neural network (LSTM) is able to
capture long-term and non-linear dynamics of time-series data, in this paper we
propose a CNNLSTM hybrid framework to fully explore the temporal-spatial
information in sEMG. Firstly, CNN is utilized to extract deep features from
sEMG spectrum, then these features are processed via LSTM-based sequence
regression to estimate wrist kinematics. Six healthy participants are recruited
for the participatory collection and motion analysis under various experimental
setups. Estimation results in both intra-session and inter-session evaluations
illustrate that CNN-LSTM significantly outperforms CNN and conventional machine
learning approaches, particularly when complex wrist movements are activated.
