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Activity recognition is fundamental to many applications envisaged in pervasive computing, especially in smart environments where the resident’s data collected from sensors will be mapped to human activities. Previous research usually focuses on scripted or pre-segmented sequences related to activities, whereas many real-world deployments require i...
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... the size of DWN is 35 × 35. Figure 5a represents a sample of the dataset, where the annotated sensor events in the dataset include ten categories of predefined activities of daily living, while the untagged sensor events are all labeled with "Other Activity". The number of sensor events for each class of ADLs in the whole dataset is displayed in Table 1, which shows that the number among different activities varies greatly and the "Other Activity" with more than 50% sensor events dominates the dataset. ...
Citations
... In [11], the dynamic segmentation of events is proposed. It is determined using the Pearson product moment correlation (PMC) coefficient between the events [24]. PMC, or more formally ρ X,Y , is a measure of the linear correlation between two variables, X and Y (two events in our case study). ...
... The notion of dynamic segmentation operates as follows: for each incoming event, the inquiry is whether it belongs to the current segment or marks the initiation of a new segment. This determination relies on the calculation of the Pearson Product Moment Correlation (PMC) coefficient, as detailed in [24], measuring the linear correlation between two sensor events. Figure 2 illustrates an example of the identification of the beginning and the end of activities' segments, i.e., how to process the real-time dynamic segmentation. ...
As living standards improve, the growing demand for energy, comfort, and health monitoring drives the increased importance of innovative solutions. Real-time recognition of human activities (HAR) in smart homes is of significant relevance, offering varied applications to improve the quality of life of fragile individuals. These applications include facilitating autonomy at home for vulnerable people, early detection of deviations or disruptions in lifestyle habits, and immediate alerting in the event of critical situations. The first objective of this work is to develop a real-time HAR algorithm in embedded equipment. The proposed approach incorporates the event dynamic windowing based on space-temporal correlation and the knowledge of activity trigger sensors to recognize activities in the case of a record of new events. The second objective is to approach the HAR task from the perspective of edge computing. In concrete terms, this involves implementing a HAR algorithm in a “home box”, a low-power, low-cost computer, while guaranteeing performance in terms of accuracy and processing time. To achieve this goal, a HAR algorithm was first developed to perform these recognition tasks in real-time. Then, the proposed algorithm is ported on three hardware architectures to be compared: (i) a NUCLEO-H753ZI microcontroller from ST-Microelectronics using two programming languages, C language and MicroPython; (ii) an ESP32 microcontroller, often used for smart-home devices; and (iii) a Raspberry-PI, optimizing it to maintain accuracy of classification of activities with a requirement of processing time, memory resources, and energy consumption. The experimental results show that the proposed algorithm can be effectively implemented on a constrained resource hardware architecture. This could allow the design of an embedded system for real-time human activity recognition.
... In this paper, the dynamic segmentation of events is proposed. It is determined using the Pearson product moment correlation (PMC) coefficient between the events [29]. PMC, or more formally ρ X,Y , is a measure of the linear correlation between two variables, X and Y (two events in our case study). ...
... The most known methods used in sensor event segmentation for real-time HAR are time windowing techniques [29][30][31]. However, in the smart home context, sensor data are often generated in a discrete manner, so a fixed time window is not practical along the schedule. ...
Human activity recognition (HAR) is fundamental to many services in smart buildings. However, providing sufficiently robust activity recognition systems that could be confidently deployed in an ordinary real environment remains a major challenge. Much of the research done in this area has mainly focused on recognition through pre-segmented sensor data. In this paper, real-time human activity recognition based on streaming sensors is investigated. The proposed methodology incorporates dynamic event windowing based on spatio-temporal correlation and the knowledge of activity trigger sensor to recognize activities and record new events. The objective is to determine whether the last event that just happened belongs to the current activity, or if it is the sign of the start of a new activity. For this, we consider the correlation between sensors in view of what can be seen in the history of past events. The proposed algorithm contains three steps: verification of sensor correlation (SC), verification of temporal correlation (TC), and determination of the activity triggering the sensor. The proposed approach is applied to a real case study: the “Aruba” dataset from the CASAS database. F1 score is used to assess the quality of the segmentation. The results show that the proposed approach segments several activities (sleeping, bed to toilet, meal preparation, eating, housekeeping, working, entering home, and leaving home) with an F1 score of 0.63–0.99.
Recently, deep learning (DL) approaches have been extensively employed to recognize human activities in smart buildings, which greatly broaden the scope of applications in this field. Convolutional neural networks (CNN), well known for feature extraction and activity classification, have been applied for estimating human activities. However, most CNN-based techniques usually focus on divided sequences associated to activities, since many real-world employments require information about human activities in real time. In this work, an online human activity recognition (HAR) framework on streaming sensor is proposed. The methodology incorporates real-time dynamic segmentation, stigmergy-based encoding, and classification with a CNN2D. Dynamic segmentation decides if two succeeding events belong to the same activity segment or not. Then, because a CNN2D requires a multi-dimensional format in input, stigmergic track encoding is adopted to build encoded features in a multi-dimensional format. It adopts the directed weighted network (DWN) that takes into account the human spatio-temporal tracks with a requirement of overlapping activities. It represents a matrix that describes an activity segment. Once the DWN for each activity segment is determined, a CNN2D with a DWN in input is adopted to classify activities. The proposed approach is applied to a real case study: the “Aruba” dataset from the CASAS database.
