avakanski / a-deep-learning-framework-for-assessing-physical-rehabilitation-exercises Goto Github PK

For the Vicon one, there are 117 dimensions(30 joints) used, and for the Kinect one there are 88 dimensions(22 joints) used, can you tell me which joints are selected here? I could only find this kind of pic from the Internet, but seems like have different joints number from your research.

Dear author, can you share the data and score labels for the 10 movements?My reproduction is poor, there may be a problem with the data.I use the same method handles Reduced Data，but get a bad result.for example the result EX1.

Hi,
I have read your paper and I am working with your code ,
I ran every necessary functions to execute the code for Vicon Data in google Colab
Unfortunately in the last function, SpatioTemporalNN_Vicon, in part 13 of code , I have faced an error that I can not solve it.

while executing this line :
concat_trunk = TempPyramid(seq_input_trunk, seq_input_trunk_2, seq_input_trunk_4, seq_input_trunk_8, timesteps, n_dim1)

an execption rased :
ValueError Traceback (most recent call last)
in ()
54 print(conv)
55
---> 56 concat_trunk = TempPyramid(seq_input_trunk, seq_input_trunk_2, seq_input_trunk_4, seq_input_trunk_8, timesteps, n_dim1)
57 concat_left_arm = TempPyramid(seq_input_left_arm, seq_input_left_arm_2, seq_input_left_arm_4, seq_input_left_arm_8, timesteps, n_dim2)
58 concat_right_arm = TempPyramid(seq_input_right_arm, seq_input_right_arm_2, seq_input_right_arm_4, seq_input_right_arm_8, timesteps, n_dim2)

6 frames
in TempPyramid(input_f, input_2, input_4, input_8, seq_len, n_dims)
16
17 #### Recurrent layers
---> 18 x = concatenate([conv1, conv2, conv3, upsample1], axis=-1)
19 return x

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/merge.py in concatenate(inputs, axis, **kwargs)
929 A tensor, the concatenation of the inputs alongside axis axis.
930 """
--> 931 return Concatenate(axis=axis, **kwargs)(inputs)
932
933

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in call(self, *args, **kwargs)
924 if _in_functional_construction_mode(self, inputs, args, kwargs, input_list):
925 return self._functional_construction_call(inputs, args, kwargs,
--> 926 input_list)
927
928 # Maintains info about the Layer.call stack.

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in _functional_construction_call(self, inputs, args, kwargs, input_list)
1096 # Build layer if applicable (if the build method has been
1097 # overridden).
-> 1098 self._maybe_build(inputs)
1099 cast_inputs = self._maybe_cast_inputs(inputs, input_list)
1100

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in _maybe_build(self, inputs)
2641 # operations.
2642 with tf_utils.maybe_init_scope(self):
-> 2643 self.build(input_shapes) # pylint:disable=not-callable
2644 # We must set also ensure that the layer is marked as built, and the build
2645 # shape is stored since user defined build functions may not be calling

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/tf_utils.py in wrapper(instance, input_shape)
321 if input_shape is not None:
322 input_shape = convert_shapes(input_shape, to_tuples=True)
--> 323 output_shape = fn(instance, input_shape)
324 # Return shapes from fn as TensorShapes.
325 if output_shape is not None:

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/merge.py in build(self, input_shape)
517 shape[axis] for shape in shape_set if shape[axis] is not None)
518 if len(unique_dims) > 1:
--> 519 raise ValueError(err_msg)
520
521 def _merge_function(self, inputs):

ValueError: A Concatenate layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 68, 192), (None, 68, 192), (None, 34, 192), (None, 34, 192)]

would you please help me solve this problem ?
Thanks for your attention