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falling-status-classification

Data Preprocessing Results

  • 5 classes : 걷기 보통, 무게중심 우/전/좌/후
  • 5 data set files per 1 person (Each file consists of 234 timestamps)

01. Normal/Abnormal status classification

  1. Training + Test Data split
- Training(80%) : Test(20%)
- classify data into 2 classes 
  - normal : 걷기 보통
  - abnormal : others
  1. Experiment Settings
- 10명의 data 통합
- Evaluation metric: recall, precision, F1 measure
  1. Modelling
- Classifier : RNN-based
  1. Results
- Accuracy : 79.01%

02. 5 classes Classification

  1. Training + Test Data split
- Training(4 files for each subject, 46805) : Test(1 file for each subject, 11700)
- classify data into 5 classes (label : 0-4)
  - normal : 걷기 보통 (0)
  - abnormal_l,abnormal_r,abnormal_f,abnormal_b, : 무게 중심 좌측/우측/전방/후방 (1,2,3,4)
  1. Experiment Settings
- 10명의 data 통합
- Evaluation metric: accuracy
  1. Modelling
- Classifier : RNN-Based (Bi-LSTM, GRU, Bi-GRU)
  1. Results (Baseline Model)
- Accuracy : 40.5% ( Simple LSTM, epochs=20, batch_size=64)
  1. Hyperparameters Optimization Results

- Highest Accuracy (91.19%) _ _ _ ( Bi-LSTM | timestamp 39 step 13 | batch 39 epoch 500 nodes 100-20 )

  • timestamp-step = 39-13

- nodes 100-20 ( Highest Accuracy: 91.19% )

Bi-LSTM GRU Bi-GRU
# of batches (#of Epoches) train test train test train test
2(100) 99.80 88.29 TBD TBD TBD TBD
39 (500) 100.00 91.19 100.00 88.29 TBD TBD
117 (1000) 100.00 88.96 100.00 87.96 TBD TBD
234 (1000) 100.00 86.07 100.00 86.73 TBD TBD
468(1500) 98.92 84.50 TBD TBD TBD TBD
729(2000) 99.53 83.61 TBD TBD TBD TBD

- nodes 100-5 ( Highest Accuracy: 90.97% )

Bi-LSTM GRU Bi-GRU
# of batches (#of Epoches) train test train test train test
2(100) TBD TBD 99.78 87.63 99.83 88.52
13 (500) TBD TBD 100.00 90.97 99.92 88.29
39 (500) TBD TBD 99.97 87.63 100.00 90.41
117 (1000) TBD TBD 100.00 84.95 98.64 85.95
234 (1000) TBD TBD 100.00 84.06 100.00 87.85
  • timestamp-step = 117 - 39 ( Highest Accuracy: 85.52% )
Bi-LSTM GRU Bi-GRU
# of batches (#of Epoches) train test train test train test
39 (200) TBD TBD 88.56 77.10 TBD TBD
117 (500) TBD TBD 100.00 85.52 TBD TBD
234 (1000) TBD TBD 97.00 80.81 TBD TBD
468 TBD TBD TBD TBD TBD TBD
972 TBD TBD TBD TBD TBD TBD
  • timestamp-step = 234 - 234 ( Highest Accuracy:63.64% )
LSTM Bi-LSTM GRU
# of Epoches train test train test train test
500 44.50 40.00 TBD TBD TBD TBD
1000 92.00 51.00 98.77 51.78 TBD TBD
1500 92.50 34.60 99.87 66.32 TBD TBD
# of batches
117 TBD TBD 100.00 62.63 TBD TBD
234 91.80 40.70 100.00 63.64 TBD TBD
468 91.80 44.00 98.00 61.62 TBD TBD
729 TBD TBD 94.75 58.59 TBD TBD
  • timestamp - step = 234 -117 ( Highest Accuracy: 77.49 %)
Bi-LSTM GRU Bi-GRU
# of batches train test train test train test
39 100.00 68.37 100.00 76.53 100.00 71. 43
117 100.00 73.47 99.49 77.49 99.25 56.12
234 100.00 71.43 86.46 59.18 100.00 56.12
468 98.24 59.18 99.24 75.51 100.00 63.27
972 100.00 69.39 94.73 65.30 100.00 54.08
  • timestamp - step = 117 -117 (Highest Accuracy: 72.73 %)
Bi-LSTM GRU Bi-GRU
# of batches train test train test train test
117 TBD TBD 99.75 72.727 TBD TBD
234 97.20 52.80 97.00 71.43 TBD TBD
468 92.50 49.00 95.75 64.64 TBD TBD
972 TBD TBD 90.23 61.22 TBD TBD
  1. Discussion

1) Bi-LSTM > LSTM

  • 양방향 : 주기 데이터에 적합
  • some additional features associated with data that might be captured by BiLSTM but unidirectional LSTM models are not capable of exposing them, since the training is only one way.

S. Siami-Namini, N. Tavakoli and A. S. Namin, "The Performance of LSTM and BiLSTM in Forecasting Time Series," 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019, pp. 3285-3292.

2) GRU > LSTM

  • GRU는 3개 gate (forget, input, output)로 이뤄진 LSTM의 simplified 버전으로 2개 gate로 구성. (update, reset)
  • For tasks with a few data
  • 적은 파라미터를 가진 경우 LSTM 보다 좋은 결과를 얻는 것으로 알려짐.

Jiang, Zheng & Hu, Meng-Han & Fan, Lei & Pan, Yaling & Tang, Wei & Zhai, Guangtao & Lu, Yong-Zai. (2020). Combining Visible Light and Infrared Imaging for Efficient Detection of Respiratory Infections such as COVID-19 on Portable Device.

  • baseline model은 9개 features 가진 HAR data
  • 실험 데이터는 x,y,z 축 가속도 3개 features의 상대적으로 단순한 data로, 보다 간소한 network에서 성능이 우수함을 확인

3) timestamp - step

  • data processing 과정에서 timestamp - step 크기를 작게할 때 성능이 향상되었음.
  • timestamp 구간이 겹칠 때 더 우수한 성능을 보였음.
  • 모델의 nodes 수가 클 때보다 작을 때 성능이 향상되었음.

4) RNN-Based Model의 Optimal parameters 에 대한 추가 분석 진행 예쩡

  • data preprocessing
  • modeling

03. Rule-based Classification

  1. Baseline rule
- features
- thresholds