A review on applications of activity recognition systems with regard to performance and evaluation

Ranasinghe, Suneth; Machot, Fadi Al; Mayr, Heinrich C.

doi:10.1177/1550147716665520

Cited by 178 publications

(83 citation statements)

References 110 publications

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“…Activity identification has been a major research focus in several industries such as surveillance and security systems, healthcare monitoring, home and work automation, human computer interaction, and context-aware behavior analysis (Lockhart et al, 2012;Ann and Theng, 2014;Ranasinghe et al, 2016). As opposed to the aforementioned applications where most studies target human activity identification (HAR), researchers in the construction engineering and management domain (CEM) have applied similar approaches to recognize, track, and identify activities performed by construction resources (both human workers and mechanical equipment).…”

Section: Background and Related Workmentioning

confidence: 99%

Automated Activity Identification for Construction Equipment Using Motion Data From Articulated Members

Rashid

Louis

2020

Front. Built Environ.

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Real-time monitoring and assessment of construction resources have always been a challenge due to the unique, dynamic, and complex nature of each construction site and operation. The ability to automatically classify activities performed by various equipment in real time can aid in making timely tactical operational decisions that can lead to increased fleet productivity, reduced time and cost of operations, and minimized idle times. Such endeavors have traditionally been performed manually through human observation, making it extremely labor and time-intensive. Meanwhile, the development of low-cost micro-electro-mechanical systems (MEMS) with rapidly evolving computing, networking, and storage capabilities, along with the advances in computational techniques such as machine learning present new opportunities in the real-time activity identification domain. Even though previous studies have shown promising results for equipment activity identification at limited levels of detail, they have a fundamental limitation in their reliance on the equipment vibration. Equipment vibration is highly dependent on factors that are extrinsic to the performance of an activity itself such as ground conditions, age and condition of equipment, and operator skill. This aspect of current methods necessitates the collection of training data from the specific equipment of interest and requires manual labeling of training data, thereby limiting its application across different types of equipment and operating conditions. This paper investigates the use of activity-specific equipment motions instead of vibration for activity identification. This approach is the first step toward the larger goal of generating training data automatically from virtual kinematic models of equipment in the future. This paper also provides an array of sensitivity analyses in order to determine the most appropriate parameters for implementing machine learning algorithms for equipment activity identification. A case study was performed using an excavator working on an earthmoving site that demonstrated a significant improvement in equipment activity identification results by utilizing inertial measurement unit (IMU) data of different articulated elements over previous efforts. The results of this paper indicate that more accurate results for activity identification can be obtained by using articulated equipment motion over vibration, which paves the way for the automatic generation of labeled training data in the future.

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Section: Background and Related Workmentioning

confidence: 99%

Automated Activity Identification for Construction Equipment Using Motion Data From Articulated Members

Rashid

Louis

2020

Front. Built Environ.

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“…Kose et.al [17] paper uses the built-in accelerometers to evaluate the efficiency of various online AR classification methods on smart phones. AR is one of many mobile technology sub-domains which has developed rapidly over the previous few years [18]. These fields of use include smart homes [19], fitness tracking, healthcare observing system [20], protection and security applications [21], tele-immersion applications [22], etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Design of LSTM-RNN on a Sensor Based HAR using Android Phones

Arthishwari*,

Anand

2020

IJRTE

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Activity Recognition (AR) is monitoring the liveliness of a person by using smart phone. Smart phones are used in a wider manner and it becomes one of the ways to identify the human’s environmental changes by using the sensors in smart mobiles. Smart phones are equipped in detecting sensors like compass sensor, gyroscope, GPS sensor and accelerometer. Human Activity Recognition (HAR) framework collects the raw data from sensors and observes the human movement using different classification methods. This paper focuses for Activity Recognition (AR) based on smart phone by analyzing the performance of various Deep Learning (DL) approach using in-built gyroscope and accelerometers. In this work, HAR dataset can be utilized from UCI based Machine Learning repository. The sensors such as gyroscope and accelerator are used to record the signals and performs various activities namely walkingdownstairs, walking-upstairs, jogging, standing, walking and sitting while a user wearing the smartphone in a pocket. The performance metrics has analyzed to recognize user’s activities using DL approach namely Recurrent Neural Network with Long-Short Term Memory (RNN- LSTM) were applied. The result provides 96% better accuracy for RNN-LSTM with minimum Mean Absolute Percentage Error (MAPE) when compare to other machine learning classifier.

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“…Simple contact sensors and motion sensors can be used here to collect information on activities occurring around the home, such as doors and cupboards opening and closing, and people moving around the home. Activity Recognition is used to recognize these activities of daily living and interpret events taking place in a smart home [3]. These activity recognition systems use this contextual information to understand the behaviors taking place within the environment [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…Activity Recognition is used to recognize these activities of daily living and interpret events taking place in a smart home [3]. These activity recognition systems use this contextual information to understand the behaviors taking place within the environment [3,4]. As this information is personal and can identify different activities and behaviors about an individual's personality, it is difficult to extract this information [5].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Windowing Approaches in Dense Sensing Environments

Quigley

Donnelly

et al. 2018

UCAmI 2018

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Windowing is an established technique employed within dense sensing environments to extract relevant features from sensor data streams. Among the established approaches of Explicit, Time-based and Sensor-Event based windowing, Dynamic windowing approaches are beginning to emerge. These dynamic approaches claim to address the inherent shortcomings of the aforementioned established approaches by determining the appropriate window length for live sensor data streams in real-time, thereby offering the potential to optimize and increase the recognition of these sensor represented activities. Beyond these potential benefits, dynamic approaches can also support anomaly detection by actively uncovering new, unknown window patterns within a trained model. This paper presents findings from a study which utilizes data from a single source dataset, towards benchmarking and comparing more traditional windowing approaches against a dynamic windowing approach. The experiments conducted on a real-world smart home dataset suggest Time-based windowing is the best approach. Through evaluation of results, Dynamic windowing approaches may benefit from carefully annotated datasets.

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A review on applications of activity recognition systems with regard to performance and evaluation

Cited by 178 publications

References 110 publications

Automated Activity Identification for Construction Equipment Using Motion Data From Articulated Members

Automated Activity Identification for Construction Equipment Using Motion Data From Articulated Members

Design of LSTM-RNN on a Sensor Based HAR using Android Phones

A Comparative Analysis of Windowing Approaches in Dense Sensing Environments

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