This paper introduces our methodology to estimate sidewalk accessibilities
from wheelchair behavior via a triaxial accelerometer in a smartphone installed
under a wheelchair seat. Our method recognizes sidewalk accessibilities from
environmental factors, e.g. gradient, curbs, and gaps, which influence
wheelchair bodies and become a burden for people with mobility difficulties.
This paper developed and evaluated a prototype system that visualizes sidewalk
accessibility information by extracting knowledge from wheelchair acceleration
using deep neural networks. Firstly, we created a supervised convolutional
neural network model to classify road surface conditions using wheelchair
acceleration data. Secondly, we applied a weakly supervised method to extract
representations of road surface conditions without manual annotations. Finally,
we developed a self-supervised variational autoencoder to assess sidewalk
barriers for wheelchair users. The results show that the proposed method
estimates sidewalk accessibilities from wheelchair accelerations and extracts
knowledge of accessibilities by weakly supervised and self-supervised
approaches.

本论文介绍了使用三轴加速度计通过电动轮椅的行为估计人行道可达性的方法，通过使用深度神经网络从轮椅加速度中提取知识，通过有监督和弱监督方法对道路表面条件进行分类，并通过无监督变分自编码器评估轮椅使用者的人行道障碍，从而估计行人道可达性。

深度神经网络对轮椅行为的识别，用于可视化人行道无障碍通行

Wheelchair Behavior Recognition for Visualizing Sidewalk Accessibility  by Deep Neural Networks

Accelerometer-based (and by extension other inertial sensors) research for
Human Activity Recognition (HAR) is a dead-end. This sensor does not offer
enough information for us to progress in the core domain of HAR - to recognize
everyday activities from sensor data. Despite continued and prolonged efforts
in improving feature engineering and machine learning models, the activities
that we can recognize reliably have only expanded slightly and many of the same
flaws of early models are still present today. Instead of relying on
acceleration data, we should instead consider modalities with much richer
information - a logical choice are images. With the rapid advance in image
sensing hardware and modelling techniques, we believe that a widespread
adoption of image sensors will open many opportunities for accurate and robust
inference across a wide spectrum of human activities.
In this paper, we make the case for imagers in place of accelerometers as the
default sensor for human activity recognition. Our review of past works has led
to the observation that progress in HAR had stalled, caused by our reliance on
accelerometers. We further argue for the suitability of images for activity
recognition by illustrating their richness of information and the marked
progress in computer vision. Through a feasibility analysis, we find that
deploying imagers and CNNs on device poses no substantial burden on modern
mobile hardware. Overall, our work highlights the need to move away from
accelerometers and calls for further exploration of using imagers for activity
recognition.

本文阐述了加速度传感器在人体活动识别领域（HAR）上存在的问题，提出图像传感器替代加速度传感器成为人体活动识别的默认传感器，并挖掘了图像传感器与卷积神经网络在人体活动识别方面的潜力。

加速度计在活动识别中是否已经走到了尽头？

Are Accelerometers for Activity Recognition a Dead-end?

Traditional human activity recognition (HAR) based on time series adopts
sliding window analysis method. This method faces the multi-class window
problem which mistakenly labels different classes of sampling points within a
window as a class. In this paper, a HAR algorithm based on U-Net is proposed to
perform activity labeling and prediction at each sampling point. The activity
data of the triaxial accelerometer is mapped into an image with the single
pixel column and multi-channel which is input into the U-Net network for
training and recognition. Our proposal can complete the pixel-level gesture
recognition function. The method does not need manual feature extraction and
can effectively identify short-term behaviors in long-term activity sequences.
We collected the Sanitation dataset and tested the proposed scheme with four
open data sets. The experimental results show that compared with Support Vector
Machine (SVM), k-Nearest Neighbor (kNN), Decision Tree(DT), Quadratic
Discriminant Analysis (QDA), Convolutional Neural Network (CNN) and Fully
Convolutional Networks (FCN) methods, our proposal has the highest accuracy and
F1-socre in each dataset, and has stable performance and high robustness. At
the same time, after the U-Net has finished training, our proposal can achieve
fast enough recognition speed.

本文提出了一种基于 U-Net 的 HAR 算法，通过将加速度计的活动数据映射为具有单像素列和多通道的图像输入到神经网络进行训练和识别，从而实现像素级姿态识别，无需手动特征提取，能够有效识别长期活动序列中的短期行为，与其他六种机器学习方法相比，该算法在各数据集中具有最高的准确性和 F1 得分，并具有稳定表现和高鲁棒性，同时实现了快速识别速度。