This method gives a differentiable model for tabular ergonomic assessment methods like Rapid Entire Body Assessment(REBA) or Rapid Upper Limb Assessment(RULA) methods. This is a Deep neural network model which is trained from the REBA table values as a dataset. The proposed method is validated by comparing its results with "dREBA" method[1] which is a wellknown method in literature for modeling the REBA table.
This is a neural network consists of 6 layer of local network (neck,trunk,leg,upper arms,lower arms, wrists) and finally with an aggregator network which returns the total REBA score based on joints degree of the local networks.
For training each model, i.e., local networks and aggregator network, there is a function with the name format of <name_of_body_part>_training_model inside ./main.py (under the root folder). For example, for the neck part, this function is dubbed neck_training_model or for the total aggregated network total_reba_from_partial_training_model. Also, for the super model, combining aggregator and local networks, you can find super_model_train function. The only need for creating models is calling corresponding models in the ./main.py file, like the following:
neck_training_model()
trunk_training_model()
leg_training_model()
upper_arm_training_model()
lower_arm_training_model()
total_reba_from_partial_training_model()
generate_super_model_training_data()
super_model_train()Note that for training the super model, we are using 4 billion generated data.
As generating this data volume is time-consuming, and keeping it in the memory is not viable,
first, we must call generate_super_model_training_data() by chunking data into the small files (near million data samples in each file).
Then, by reading them, train the super model.
Also, if you need to modfiy the topology or data ranges for each netwrok, you can find a function with the name format of <body_part_name>_learning_model and <body_part_name>_learning_ranges, respectively, in the ./main.py file.
For the super model, combining the networks is done by create_super_model function.
Based on this part of the experimentation, the set of selected hyper-paramters are represented in the following table:
Note that the dropout rate is being included as a regularization term for hindering a well-known phenomenon in machine learning called overfitting [2]. However, the rate 0.0 has been selected for all networks in our experiment, based on the grid searching optimization.
For using pre-trained models, you can loda the specified model under the data folder (in root folder). To load models, you can use the following command:
from tensorflow.keras.models import load_model
model = load_model('<address to the specified model>')For example, from the ./main.py function (under root folder), you can load neck local network (for predicting local neck REBA) by the following code snippet:
neck_model = load_model('./data/neck_DNN.model')Or for loading the super model (for predicting the total REBA score) use the the following code snippet:
super_model = load_model('./data/super_model_DNN.model')After loading the model, you can do the approximation by the following call, for example the super model prediction:
num_of_data = 2
data = {
'neck_model_input': np.zeros(shape=(num_of_data, 3)),
'trunk_model_input': np.zeros(shape=(num_of_data, 3)),
'leg_model_input': np.zeros(shape=(num_of_data, 1)),
'upper_arm_model_input': np.zeros(shape=(num_of_data, 6)),
'lower_arm_model_input': np.zeros(shape=(num_of_data, 2)),
'wrist_model_input': np.zeros(shape=(num_of_data, 6))
}
data['neck_model_input'][:, :] = [[10, 10, 10], [20, 20, 20]]
data['trunk_model_input'][:, :] = [[10, 10, 10], [20, 20, 20]]
data['leg_model_input'][:, :] = [[10], [20]]
data['upper_arm_model_input'][:, :] = [[10, 10, 10, 10, 10, 10], [20, 20, 20, 20, 20, 20]]
data['lower_arm_model_input'][:, :] = [[10, 10], [20, 20]]
data['wrist_model_input'][:, :] = [[10, 10, 10, 10, 10, 10], [20, 20, 20, 20, 20, 20]]
pred = super_model.predict(data)
pred = list(chain(*pred)) # list of two REBA scores for two input body joints For comparing the reuslts with the dREBA, we need to implemeent the method first.
As the dREBA method is analytical, we have implemented it with MATLAB.
You can find the corresponding files in ./dREBA/matlab/ folder.
For training and testing, we need to generate data.
For this matter, you can run ./dREBA/main.py.
This run generates required data for training and testing in ./dREBA/matlab/data/input/ folder, called M.csv and N.csv for training, and
M_test.csv and N_test.csv, respectively. M files are the sampls of body joints in each row, and N files are the REBA scores for each sample in the row of M files.
Afterwards, running ./dREBA/matlab/optimizer_tobecontinued.m will output test results on the random generated data in ./dREBA/matlab/dat/output/ folder.
Finally, to have test errors on the super model, you can run ./main.py to get the results on ./data folder with the names of neuro_errors.csv and neuro_estimations.csv that are the test error, and the approximated REBA score on the generated data, respectively.
There is a chance for utilizing the learning models in a task optimization. The required forward kinematics for human body
has been implemented in ./human_forward_kinematic.py and obejective function for this optimiztion task that returns the sum of gradients of the risk (REBA score given by NeuroErgo) for the human worker, and forward kinematics of the human body and a target that can be the target position for an instrument in the factory floor. You can find this function in ./main.py, dubbed objective_function.
For the optimization, we can use the localsolver library which comprises a balck-box optimization. You can find the example in the following:
# first load the super model
super_model = load_model('./data/super_model_DNN.model')
# allowd body joint ranges (domain-oriented)
qss = [[-60,30], [-54,54], [-60,60],\
[-30,60], [-40, 40], [-35, 35],\
[0,60],\
[-20,45], [-20, 0, 20, 45], [-2,0], [-2,0], [0, 30], [0, 30],\
[0,100], [0, 100],\
[-53,15], [-53,15], [-40, 30], [-40, 30], [-90, 90], [-90, 90]]
with localsolver.LocalSolver() as ls:
model = ls.get_model()
for i, qs in enumerate(qss):
minimum = min(qs)
maximum = max(qs)
globals()['x%s' % i] = eval(f'model.float({minimum},{maximum})')
f = model.create_double_blackbox_function(objective_function)
call = model.call()
call.add_operand(f)
for i in range(len(qss)):
eval(f'call.add_operand(x{i})')
model.minimize(call)
model.close()
ls.get_param().set_time_limit(50)
ls.solve()
sol = ls.get_solution()
for i in range(len(qss)):
eval('print("x{} = {}".format('+ str(i) + ',sol.get_value(x' + str(i) +')))')
# the ouput of the solver is optimized body joint degrees that minimized risk,
# besides the distance of human forward kinematic and the target
print("obj = {}".format(sol.get_value(call)))[1] Busch, Baptiste and Maeda, Guilherme and Mollard, Yoan and Demangeat, Marie and Lopes, Manuel (2017). Postural optimization for an ergonomic human-robot interaction. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),
[2] Goodfellow, I., Bengio, Y. and Courville, A., 2016. Deep learning. MIT press.