A Comparison of Machine Learning and Deep Learning Techniques for Activity Recognition using Mobile Devices

Abstract

We have compared the performance of different machine learning techniques for human activity recognition. Experiments were made using a benchmark dataset where each subject wore a device in the pocket and another on the wrist. The dataset comprises thirteen activities, including physical activities, common postures, working activities and leisure activities. We apply a methodology known as the activity recognition chain, a sequence of steps involving preprocessing, segmentation, feature extraction and classification for traditional machine learning methods; we also tested convolutional deep learning networks that operate on raw data instead of using computed features. Results show that combination of two sensors does not necessarily result in an improved accuracy. We have determined that best results are obtained by the extremely randomized trees approach, operating on precomputed features and on data obtained from the wrist sensor. Deep learning architectures did not produce competitive results with the tested architecture.

Classical Machine Learning Versus Deep Learning for the Older Adults Free-Living Activity Classification

“…This result could be related to the fact that a plateau in performance was reached, suggesting that after reaching a certain level of performance, further enhancement might not be possible, regardless of which of the two machine learning approaches is used, as there is a narrow range for improvement and from which to differentiate between the performances of the various PAC systems. Recently, Baldominos et al [ 36 ] performed a similar type of analysis to observe classical machine learning performance versus CNN-based PAC systems (although they analysed the ADLs of younger adults in a constrained environment, rather than in free-living conditions, and they used a CNN instead of an LSTM network). They concluded that the classical machine learning PAC system performed better than the deep learning-based PAC system, which suggests that deep learning methods are not always optimal when referring to wearable sensors based on physical activity classification systems.…”

“…Using a fully validated free-living dataset of older adults’ ADLs, we aim to compare classical ML-based PAC systems and deep learning-based PAC systems. Recently, only a couple of studies [ 35 , 36 ] have investigated the performance of classical ML versus deep learning algorithms. Nevertheless, these studies focused on young adults performing ADLs in a laboratory-constrained environment.…”

CT-Based Deep Learning Model for Invasiveness Classification and Micropapillary Pattern Prediction Within Lung Adenocarcinoma

“…Deep learning, as a branch of AI, has emerged due to its unprecedented superior performance in recent image classification competitions. With the use of graphics processing unit (GPU) hardware, the deep learning model can arrange a much larger scale of the dataset and can achieve higher accuracy and stability than the traditional machine learning technique, which has been illustrated in many other fields (13,14). Deep learning AI can be used as a computer-aided diagnostic system, and can become a part of the clinical diagnostic procedure.…”

LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes

“…When applied to time-series classification-related HAR, the CNN has superiority over other conventional ML approaches, due to its local dependency and scale invariance [ 26 ]. Studies on one-dimensional CNNs have shown that these DL models are more effective in solving the HAR problem with performance metrics than conventional ML models [ 27 ]. Due to the temporal dependency of sensor time-series data, LSTM networks are introduced to tackle the issue.…”