“Fortune at the Bottom of the Classifier Pyramid”: A Novel Approach to Human Activity Recognition

Human activity recognition is an extensively researched topic in the last decade. Recent methods employ supervised and unsupervised deep learning techniques in which spatial and temporal dependency is modeled. This paper proposes a novel approach for human activity recognition using skeleton data. The method combines supervised and unsupervised learning algorithms in order to provide qualitative results and performance in real time. The proposed method involves a two-stage framework: the first stage applies an unsupervised clustering technique to group up activities based on their similarity, while the second stage classifies data assigned to each group using graph convolutional networks. Different clustering techniques and data augmentation strategies are explored for improving the training process. The results were compared against the state of the art methods and the proposed model achieved 90.22% Top-1 accuracy performance for NTU-RGB+D dataset (the performance was increased by approximately 9% compared with the baseline graph convolutional method). Moreover, inference time and total number of parameters stay within the same magnitude order. Extending the initial set of activities with additional classes is fast and robust, since there is no required retraining of the entire architecture but only to retrain the cluster to which the activity is assigned.

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Exploration of Human Activity Recognition Using a Single Sensor for Stroke Survivors and Able-Bodied People

Sensors ◽

10.3390/s21030799 ◽

2021 ◽

Vol 21 (3) ◽

pp. 799

Author(s):

Long Meng ◽

Anjing Zhang ◽

Chen Chen ◽

Xingwei Wang ◽

Xinyu Jiang ◽

...

Keyword(s):

Activity Recognition ◽

Human Activity ◽

Standard Error ◽

Human Activity Recognition ◽

Support Vector ◽

Stroke Survivors ◽

Extensor Carpi Ulnaris ◽

Novel Approach ◽

Sensor Position ◽

Single Sensor

Commonly used sensors like accelerometers, gyroscopes, surface electromyography sensors, etc., which provide a convenient and practical solution for human activity recognition (HAR), have gained extensive attention. However, which kind of sensor can provide adequate information in achieving a satisfactory performance, or whether the position of a single sensor would play a significant effect on the performance in HAR are sparsely studied. In this paper, a comparative study to fully investigate the performance of the aforementioned sensors for classifying four activities (walking, tooth brushing, face washing, drinking) is explored. Sensors are spatially distributed over the human body, and subjects are categorized into three groups (able-bodied people, stroke survivors, and the union of both). Performances of using accelerometer, gyroscope, sEMG, and their combination in each group are evaluated by adopting the Support Vector Machine classifier with the Leave-One-Subject-Out Cross-Validation technique, and the optimal sensor position for each kind of sensor is presented based on the accuracy. Experimental results show that using the accelerometer could obtain the best performance in each group. The highest accuracy of HAR involving stroke survivors was 95.84 ± 1.75% (mean ± standard error), achieved by the accelerometer attached to the extensor carpi ulnaris. Furthermore, taking the practical application of HAR into consideration, a novel approach to distinguish various activities of stroke survivors based on a pre-trained HAR model built on healthy subjects is proposed, the highest accuracy of which is 77.89 ± 4.81% (mean ± standard error) with the accelerometer attached to the extensor carpi ulnaris.

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A novel approach for human activity recognition using object interactions and machine learning

10.1109/sas51076.2021.9530029 ◽

2021 ◽

Author(s):

Marc Schroth ◽

Timucin Etkin ◽

Wilhelm Stork

Keyword(s):

Machine Learning ◽

Activity Recognition ◽

Human Activity ◽

Human Activity Recognition ◽

Novel Approach ◽

Object Interactions

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Exploiting Smart Meter Power Consumption Measurements for Human Activity Recognition (HAR) with a Motif-Detection-Based Non-Intrusive Load Monitoring (NILM) Approach

Sensors ◽

10.3390/s21238036 ◽

2021 ◽

Vol 21 (23) ◽

pp. 8036

Author(s):

Sebastian Wilhelm ◽

Jakob Kasbauer

Keyword(s):

Power Consumption ◽

Activity Recognition ◽

Human Activity ◽

Human Activity Recognition ◽

Smart Meter ◽

Activity Data ◽

Motif Detection ◽

Novel Approach ◽

Load Monitoring ◽

Individual Household

Numerous approaches exist for disaggregating power consumption data, referred to as non-intrusive load monitoring (NILM). Whereas NILM is primarily used for energy monitoring, we intend to disaggregate a household’s power consumption to detect human activity in the residence. Therefore, this paper presents a novel approach for NILM, which uses pattern recognition on the raw power waveform of the smart meter measurements to recognize individual household appliance actions. The presented NILM approach is capable of (near) real-time appliance action detection in a streaming setting, using edge computing. It is unique in our approach that we quantify the disaggregating uncertainty using continuous pattern correlation instead of binary device activity states. Further, we outline using the disaggregated appliance activity data for human activity recognition (HAR). To evaluate our approach, we use a dataset collected from actual households. We show that the developed NILM approach works, and the disaggregation quality depends on the pattern selection and the appliance type. In summary, we demonstrate that it is possible to detect human activity within the residence using a motif-detection-based NILM approach applied to smart meter measurements.

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