# Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors

arXiv: https://arxiv.org/abs/1708.08989 · HTML: https://mlsanity.com/paper/1708.08989/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2017.

## Built on by (most cited first, 24 of 24)

- [Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances](https://mlsanity.com/paper/2111.00418/) (2021)
- [Cross-domain Activity Recognition via Substructural Optimal Transport](https://mlsanity.com/paper/2102.03353/) (2021)
- [Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognition](https://mlsanity.com/paper/2203.07910/) (2022)
- [A Close Look into Human Activity Recognition Models using Deep Learning](https://mlsanity.com/paper/2204.13589/) (2022)
- [Progressive Cross-modal Knowledge Distillation for Human Action Recognition](https://mlsanity.com/paper/2208.08090/) (2022)
- [Casual Conversations v2: Designing a large consent-driven dataset to measure algorithmic bias and robustness](https://mlsanity.com/paper/2211.05809/) (2022)
- [Investigating the significance of adversarial attacks and their relation to interpretability for radar-based human activity recognition systems](https://mlsanity.com/paper/2101.10562/) (2021)
- [Temporal Feature Alignment in Contrastive Self-Supervised Learning for Human Activity Recognition](https://mlsanity.com/paper/2210.03382/) (2022)
- [Otago Exercises Monitoring for Older Adults by a Single IMU and Hierarchical Machine Learning Models](https://mlsanity.com/paper/2310.03512/) (2023)
- [Disentangling Imperfect: A Wavelet-Infused Multilevel Heterogeneous Network for Human Activity Recognition in Flawed Wearable Sensor Data](https://mlsanity.com/paper/2402.09434/) (2024)
- [Octave Mix: Data augmentation using frequency decomposition for activity recognition](https://mlsanity.com/paper/2101.02882/) (2021)
- [Real-Time Violence Detection Using CNN-LSTM](https://mlsanity.com/paper/2107.07578/) (2021)
- [randomHAR: Improving Ensemble Deep Learners for Human Activity Recognition with Sensor Selection and Reinforcement Learning](https://mlsanity.com/paper/2307.07770/) (2023)
- [Decoding Human Activities: Analyzing Wearable Accelerometer and Gyroscope Data for Activity Recognition](https://mlsanity.com/paper/2310.02011/) (2023)
- [A Multi-Stage Temporal Convolutional Network for Volleyball Jumps Classification Using a Waist-Mounted IMU](https://mlsanity.com/paper/2310.13097/) (2023)
- [DS-MS-TCN: Otago Exercises Recognition with a Dual-Scale Multi-Stage Temporal Convolutional Network](https://mlsanity.com/paper/2402.02910/) (2024)
- [Hysteretic Behavior Simulation Based on Pyramid Neural Network:Principle, Network Architecture, Case Study and Explanation](https://mlsanity.com/paper/2206.03990/) (2022)
- [Human Activity Recognition on Time Series Accelerometer Sensor Data using LSTM Recurrent Neural Networks](https://mlsanity.com/paper/2206.07654/) (2022)
- [Explaining, Analyzing, and Probing Representations of Self-Supervised Learning Models for Sensor-based Human Activity Recognition](https://mlsanity.com/paper/2304.07304/) (2023)
- [Sequence-to-Sequence Forecasting-aided State Estimation for Power Systems](https://mlsanity.com/paper/2305.13215/) (2023)
- [Unified Contrastive Fusion Transformer for Multimodal Human Action Recognition](https://mlsanity.com/paper/2309.05032/) (2023)
- [rTsfNet: a DNN model with Multi-head 3D Rotation and Time Series Feature Extraction for IMU-based Human Activity Recognition](https://mlsanity.com/paper/2310.19283/) (2023)
- [A Masked Semi-Supervised Learning Approach for Otago Micro Labels Recognition](https://mlsanity.com/paper/2405.12711/) (2024)
- [Semantically Encoding Activity Labels for Context-Aware Human Activity Recognition](https://mlsanity.com/paper/2504.07916/) (2025)

## Abstract (arXiv)

Human activity recognition (HAR) has become a popular topic in research because of its wide application. With the development of deep learning, new ideas have appeared to address HAR problems. Here, a deep network architecture using residual bidirectional long short-term memory (LSTM) cells is proposed. The advantages of the new network include that a bidirectional connection can concatenate the positive time direction (forward state) and the negative time direction (backward state). Second, residual connections between stacked cells act as highways for gradients, which can pass underlying information directly to the upper layer, effectively avoiding the gradient vanishing problem. Generally, the proposed network shows improvements on both the temporal (using bidirectional cells) and the spatial (residual connections stacked deeply) dimensions, aiming to enhance the recognition rate. When tested with the Opportunity data set and the public domain UCI data set, the accuracy was increased by 4.78% and 3.68%, respectively, compared with previously reported results. Finally, the confusion matrix of the public domain UCI data set was analyzed.