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AROMA: A Deep Multi-Task Learning Based Simple and Complex Human Activity Recognition Method Using Wearable Sensors

Python 100.00%
cnn deep-neural-networks human-activity-recognition lstm-networks multi-task-learning wearable-devices

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har-joint-model's Issues

Pre-processing for Opportunity dataset - v2.0 release

Hello,
I am trying to use the OpportunityDataset class (in data1.py) to parse the files in the dataset using the configuration below but it is not working. It cannot reshape the X tensor correctly at line 214
return np.reshape(res, (int(len(res) / win), win, self.config.f_num))

I was wondering how do you reduce the number of channels to 113 and which sensors are selected.
Thanks!

Config:

class OpportunityConfig(object):
    def __init__(self):
        ''' Without unlabeled data
        '''
        self.channels = 113
        self.f_num = self.channels
        self.overlap_ratio = 0.5
        self.s_win_size = 24
        self.c_win_size = 10  # 10
        self.s_labels_num = 4
        self.c_labels_num = 5
        self.batch_size = 64
        self.norm = False
        self.max_lr = 0.003
        self.min_lr = 0.0001
        self.decay_speed = 700
        self.iter = 2101
        self.test_point = 30
        self.dataset = data1.OpportunityDataset("OpportunityUCIDataset/dataset/", self)

Huynh.cp

I'm wondering if you could provide huynh.cp dataset or the scripts generating it? I couldn't find it online. That would be much appreciated.

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