Acceleration-based Human Activity Recognition of Packaging Tasks Using Motif-guided Attention Networks

Morales, Jaime; Yoshimura, Naoya; Xia, Qingxin; Wada, Atsushi; Namioka, Yasuo; Maekawa, Taira

doi:10.1109/percom53586.2022.9762388

Cited by 9 publications

(5 citation statements)

References 27 publications

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“…Various devices, including wearables [6] and infrared cameras [7], have been used to monitor temperature changes and predict thermal preferences. Additional data like heartbeat rates [12], obtained via smartphones [14] and in-ear microphones [15], or physical activity levels, detected through wearables [16], [17], also contribute to understanding occupants' thermal sensations. The main applicability challenges for sensor-based systems are the costs associated with widespread deployment and encouraging users to share data from personal devices.…”

Section: Related Workmentioning

confidence: 99%

Co-zyBench: Using Co-Simulation and Digital Twins to Benchmark Thermal Comfort Provision in Smart Buildings

Ma,

Panic,

Yus

et al. 2024

2024 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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Heating, Ventilation, and Air Conditioning (HVAC) systems account for 40% to 50% of energy usage in commercial buildings. Thus, innovative ways to control and manage HVAC systems while preserving occupants' comfort are required. Stateof-the-art solutions employ pervasive systems with sensors or smart devices to gauge individual thermal sensations, yet assessing these methods is challenging. Real-world experiments are expensive, limited in access, and often overlook occupant and regional diversity. To address this, we introduce Co-zyBench, a benchmark tool using a Digital Twin (DT) approach for evaluating personalized thermal comfort systems. It employs a co-simulation middleware interfacing between a DT of the smart building and its HVAC system and another DT representing occupants' dynamic thermal preferences in various spaces. The DTs that support Co-zyBench are generated based on information, including data captured by sensors, of the space in which the thermal comfort system has to be evaluated. Co-zyBench incorporates metrics for energy consumption, thermal comfort, and occupant equality. It also features reference DTs based on standard buildings, HVAC systems, and occupants with diverse thermal preferences.

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Section: Related Workmentioning

confidence: 99%

Co-zyBench: Using Co-Simulation and Digital Twins to Benchmark Thermal Comfort Provision in Smart Buildings

Ma,

Panic,

Yus

et al. 2024

2024 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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“…We perform parameter tuning over (2,4,6) blocks to derive the number of causal convolution layers to utilize (and thus to determine the receptive field of the aggregator). The kernel sizes of layers are (2,3,4,5,6,7), and setting the number of causal convolution blocks to 2 results in two blocks with the causal convolution filter sizes being (2, 3) respectively. This is extended for situations where the number of blocks is 4 or 6.…”

Section: B Aggregator Networkmentioning

confidence: 99%

“…The primary advantage of this setup is the increased parallelizability due the presence of solely convolutional architecture (across both encoder and the aggregator). The GRU with 256 units and 2 layers is replaced with causal convolutions tuned over (2,4,6) blocks (as in the case of enhanced CPC), in order to obtain the context vector. The parameter tuning protocol is otherwise identical to Section IV-C and five random runs of the best performing models are detailed as 'CPC + Conv.…”

Section: Investigating the Components Of The Cpc Frameworkmentioning

confidence: 99%

“…Sensor-based Human Activity Recognition (HAR), which involves the automatic prediction of activities being performed, has a multitude of applications, including health [1]- [3], industry [4]- [6], and fitness tracking [7], [8]. However, this process is highly dependent on the availability of annotated data, which is typically collected in laboratory settings and for orchestrated sets of activities, resulting in limited size and diversity of activities and participants [9].…”

Section: Introductionmentioning

confidence: 99%

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Contrastive Predictive Coding for Human Activity Recognition

Haresamudram

Essa

Plötz

2021

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Feature extraction is crucial for human activity recognition (HAR) using body-worn movement sensors. Recently, learned representations have been used successfully, offering promising alternatives to manually engineered features. Our work focuses on effective use of small amounts of labeled data and the opportunistic exploitation of unlabeled data that are straightforward to collect in mobile and ubiquitous computing scenarios. We hypothesize and demonstrate that explicitly considering the temporality of sensor data at representation level plays an important role for effective HAR in challenging scenarios. We introduce the Contrastive Predictive Coding (CPC) framework to human activity recognition, which captures the temporal structure of sensor data streams. Through a range of experimental evaluations on real-life recognition tasks, we demonstrate its effectiveness for improved HAR. CPC-based pre-training is self-supervised, and the resulting learned representations can be integrated into standard activity chains. It leads to significantly improved recognition performance when only small amounts of labeled training data are available, thereby demonstrating the practical value of our approach. Through a series of experiments, we also develop guidelines to help practitioners adapt and modify the framework towards other mobile and ubiquitous computing scenarios.

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“…In this study, we focus on repetition counting methods for an action using body-worn inertial sensors because they are effective in sports and industrial environments that need to record repetition data of workouts for exercise management and factory worker logs for performance verification of predefined tasks, respectively (Lukowicz et al, 2004;Maekawa et al, 2016;Xia et al, 2019Xia et al, , 2020Morales et al, 2022). Prior studies on repetition counting rely on supervised learning such as neural networks.…”

Section: Introductionmentioning

confidence: 99%

Few-Shot and Weakly Supervised Repetition Counting With Body-Worn Accelerometers

Nishino

Maekawa

Hara

2022

Front. Comput. Sci.

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This study investigates few-shot weakly supervised repetition counting of a human action such as workout using a wearable inertial sensor. We present WeakCounterF that leverages few weakly labeled segments containing occurrences of a target action from a target user to achieve precise repetition counting. Here, a weak label is defined to specify only the number of repetitions of an action included in an input data segment in this study, facilitating preparation of datasets for repetition counting. First, WeakCounterF leverages data augmentation and label diversification techniques to generate augmented diverse training data from weakly labeled data from users other than a target user, i.e., source users. Then, WeakCounterF generates diverse weakly labeled training data from few weakly labeled training data from the target user. Finally, WeakCounterF trains its repetition counting model composed of an attention mechanism on the augmented diversified data from the source users, and then fine-tunes the model on the diversified data from the target user.

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Acceleration-based Human Activity Recognition of Packaging Tasks Using Motif-guided Attention Networks

Cited by 9 publications

References 27 publications

Co-zyBench: Using Co-Simulation and Digital Twins to Benchmark Thermal Comfort Provision in Smart Buildings

Co-zyBench: Using Co-Simulation and Digital Twins to Benchmark Thermal Comfort Provision in Smart Buildings

Contrastive Predictive Coding for Human Activity Recognition

Few-Shot and Weakly Supervised Repetition Counting With Body-Worn Accelerometers

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