Yes, you are absolutely correct.
Here is a snippet for your case:
x = torch.ones((239, 49, 5))
x = x.permute((0, 2, 1))
print(x.shape)
model = nn.Conv1d(5, 5, 3)
output = model(x)
print(output.shape)

If you want to know a little more what happens in each channel, you can play with above example like this:
x = torch.ones((239, 49, 5))
x = x.permute((0, 2, 1))  # permuate feature and temporal channels
print(x.shape)
model = nn.Conv1d(5, 5, 49, groups=5, bias=False)  # remove bias and set window same as whole sequence of temporal data
nn.init.constant_(model.weight, 1.)  # set kernel to one (instead of random)
output = model(x)
print(output.shape)