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. 2022 Jan 18;55(6):4755–4808. doi: 10.1007/s10462-021-10116-x

Table 3.

Sensor-based HAR models

C1 C2 C3 C4 C5 C6 C7 C8
# Features Feature extraction ML or DL model Architecture Metrics Validation Hyper-parameters/optimizer/loss function CIT*
R1 6/Time domain Hand-crafted SVM SVM classifier for different kernels (polynomial, Radial basis function and linear) F1-score, accuracy tenfold C, ℽ & degree in grid search Garcia-Gonzalez et al. (2020)
R2 Spatial features Automatic CNN C (32) − C (64) − C (128) − P − C (128) − P − C (128) − P – FC (128) – SM Accuracy 10% data for validation LR: 0.001, BS: 50/Adam Wang et al. (2019a)
R3 Frequency domain Automatic CNN 3C with MP and dropout, 2 FC with dropout and SM F1-score, Precision, recall CV LR: 0.01, DO/Adam Lawal and Bano (2020)
R4 Time domain Automatic CNN-RNN with attention mechanism TRASEND: C1- C2- C3)- flatten and concat, merge layer- temporal information extractor using a 8-headed self-attention mechanism RNN, o/p layer F1-score Leave one user out and CV LR: {0.001, 0.0001,,00,001}/Adam/Cross Entropy Buffelli and Vandin (2020)
R5 Spatial, Temporal Automatic LSTM-CNN 2 LSTM layer (32 neurons), CNN (64), Max pooling, CNN (128), GAP, BN, o/p layer(softmax) F1-score, accuracy _ LR: 0.001/Adam/Cross entropy Xia et al. (2020)
R6 18/Time & Frequency domain Hand-crafted AdaBoost, AdaBoost-CNN, CNN-SVM For AdaBoost CNN- 4C, AP, FC, SM Accuracy Sub-out validation Experiment with and without personalization similarity Ferrari et al. (2020)
R7 225 sensory features Automatic DNN Layer 1(256), layer2 (512), layer 3 (128), O/p (softmax) Accuracy, F1-score, Specificity, Sensitivity 5% training data is used No. of layers, no. of nodes per layer, appropriate regularization function Fazli et al. (2021)
R8 Time domain Automatic CNN- CapsNet architecture SenseCapsNet: I/p, 1D C (K = 5, S = 1), Primary caps: C2(K = 5, S = 2) and squash, Activity caps where k is kernel size and S is strides Precision, recall tenfold CV mini batches:64,LR: 0.01, DO/SGD Pham et al. (2020)

CV cross validation, LOSO leave one subject out, C convolution, P pooling, AP average pooling, MP max pooling, FC fully connected, SM softmax, BN batch normalization layer, LR learning rate, DO dropout, BS batch size, SGD stochastic gradient descent, concat concatenation, Spec. specificity, Sens sensitivity, TL transfer learning, CIT citations