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A general framework for sensor-based human activity recognition

COMPUTERS IN BIOLOGY AND MEDICINE 2018年 95卷 248-260页

作者： Koeping, Lukas Shirahama, Kimiaki Grzegorzek, Marcin Univ Siegen Pattern Recognit Grp Siegen Germany Univ Econ Katowice Fac Informat & Commun Katowice Poland

Today's wearable devices like smartphones, smartwatches and intelligent glasses collect a large amount of data from their built-in sensors like accelerometers and gyroscopes. These data can be used to identify a person's current activity and in turn can be utilised for applications in the field of personal fitness assistants or elderly care. However, developing such systems is subject to certain restrictions: (i) since more and more new sensors will be available in the future, activity recognition systems should be able to integrate these new sensors with a small amount of manual effort and (ii) such systems should avoid high acquisition costs for computational power. We propose a general framework that achieves an effective data integration based on the following two characteristics: Firstly, a smartphone is used to gather and temporally store data from different sensors and transfer these data to a central server. Thus, various sensors can be integrated into the system as long as they have programming interfaces to communicate with the smartphone. The second characteristic is a codebook-based feature learning approach that can encode data from each sensor into an effective feature vector only by tuning a few intuitive parameters. In the experiments, the framework is realised as a real-time activity, recognition system that integrates eight sensors from a smartphone, smartwatch and smartglasses, and its effectiveness is validated from different perspectives such as accuracies, sensor combinations and sampling rates.

关键词： sensor-based activity recognition Feature learning sensor data collection

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A Class Incremental Temporal-Spatial Model based on Wireless sensor Networks for activity recognition 15th

A Class Incremental Temporal-Spatial Model Based on Wireless...

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15th International Conference on Wireless Algorithms, Systems, and Applications (WASA)

作者： Li, Xue Nie, Lanshun Si, Xiandong Zhan, Dechen Harbin Inst Technol Harbin Peoples R China

ISBN: (纸本)9783030590161;9783030590154

With the development of Internet of Things (IoT), all kinds of sensors appear everywhere in our lives. sensor-based Human activity recognition (HAR) as the most common application of IoT system, has attracted attentions from both academia and industry due to the popularity of IoT. In most real-life scenarios, HAR is dynamic. For instance, in the process of classifying the activities, a new class is often encountered. To effectively recognize new activities, we propose a three-stage class incremental temporal-spatial model based on wireless sensor networks for activity recognition. In the first stage, when the new class arrives, rather than using the traditional method to train all the old class data, we design a method based on the recognition score to select part of the old class data as a sample for training, which represents the data distribution of the old class data. In the second stage, we analyze the temporal and spatial characteristics of activity data in combination with physical knowledge, the model extract temporal-spatial features, which further enhances the recognition effect. In the third stage, we avoid the phenomenon of 'catastrophic forgetting' of a network by finetuning distillation loss and classification loss. To demonstrate the effectiveness of our proposed model, we conducted comparative experiments using different models on different public datasets. The results showed our proposed model can not only recognize new classes, but also maintain the recognition ability of old classes.

关键词： Class incremental sensor-based activity recognition Temporal-spatial model

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Advanced Home-based Diabetes Monitoring System: Initial Real-World Experiences 17th

Advanced Home-Based Diabetes Monitoring System: Initial Real...

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17th International Work-Conference on Artificial Neural Networks (IWANN)

作者： Diaz Jimenez, David Lopez Ruiz, Jose Luis Montoro Lendinez, Alicia Gonzalez Lama, Jesus Espinilla Estevez, Macarena Univ Jaen Dept Comp Sci Jaen Spain Cordoba Hlth Management Area Cabra Clin Management Unit Cordoba Spain Univ Cordoba Reina Sofia Univ Hosp Maimonides Biomed Res Inst Cordoba IMIBIC Cordoba Spain

ISBN: (纸本)9783031430848;9783031430855

Diabetes is a disease that requires monitoring of healthy habits for its treatment. A recent study suggests that sensor-based activity recognition approaches are a suitable tool for monitoring such habits that are established between clinical personnel and the patient through a therapeutic contract. To date, there is no fully described system architecture for implementing a sensor-based activity recognition approach to monitor healthy habits. In this paper, we present the advanced architecture of a system for monitoring healthy habits of multiple diabetes patients in their homes. The presented system, called AI2EPD, features a complex architecture that encompasses from the sensor devices deployed in each patient's home to persistence in the central server and visualisation in the technical and clinical interface. The system has been deployed in the municipality of Cabra in Cordoba (Spain) in collaboration with the Cabra Health Centre. The approach for sensor installation in the patient's home, as well as the issues and challenges encountered during system deployment, are presented in this section.

关键词： sensor-based activity recognition monitor healthy habits diabetes patients complex architectures with sensors

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HealthXAI: Collaborative and explainable AI for supporting early diagnosis of cognitive decline

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2021年 116卷 168-189页

作者： Khodabandehloo, Elham Riboni, Daniele Alimohammadi, Abbas KN Toosi Univ Technol Dept Geospatial Informat Syst Tehran Iran Univ Cagliari Dept Math & Comp Sci Cagliari Italy

Our aging society claims for innovative tools to early detect symptoms of cognitive decline. Several research efforts are being made to exploit sensorized smart-homes and artificial intelligence (AI) methods to detect a decline of the cognitive functions of the elderly in order to promptly alert practitioners. Even though those tools may provide accurate predictions, they currently provide limited support to clinicians in making a diagnosis. Indeed, most AI systems do not provide any explanation of the reason why a given prediction was computed. Other systems are based on a set of rules that are easy to interpret by a human. However, those rule-based systems can cope with a limited number of abnormal situations, and are not flexible enough to adapt to different users and contextual situations. In this paper, we tackle this challenging problem by proposing a flexible AI system to recognize early symptoms of cognitive decline in smart-homes, which is able to explain the reason of predictions at a fine-grained level. Our method relies on well known clinical indicators that consider subtle and overt behavioral anomalies, as well as spatial disorientation and wandering behaviors. In order to adapt to different individuals and situations, anomalies are recognized using a collaborative approach. We experimented our approach with a large set of real world subjects, including people with MCI and people with dementia. We also implemented a dashboard to allow clinicians to inspect anomalies together with the explanations of predictions. Results show that our system's predictions are significantly correlated to the person's actual diagnosis. Moreover, a preliminary user study with clinicians suggests that the explanation capabilities of our system are useful to improve the task performance and to increase trust. To the best of our knowledge, this is the first work that explores data-driven explainable AI for supporting the diagnosis of cognitive decline. (C) 2020 Elsevier B.V.

关键词： Pervasive healthcare Explainable artificial intelligence Cognitive decline sensor-based activity recognition

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Detecting indicators of cognitive impairment via Graph Convolutional Networks

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年第0期89卷 103401-000页

作者： Arifoglu, Damla Charif, Hammadi Nait Bouchachia, Abdelhamed Bournemouth Univ Bournemouth Dorset England

While the life expectancy is on the rise all over the world, more people face health related problems such as cognitive decline. Dementia is a name used to describe progressive brain syndromes affecting memory, thinking, behaviour and emotion. People suffering from dementia may lose their abilities to perform daily life activities and they become on their caregivers. Hence, detecting the indicators of cognitive decline and warning the caregivers and medical doctors for further diagnosis would be helpful. In this study, we tackle the problem of activity recognition and abnormal behaviour detection in the context of dementia by observing daily life patterns of elderly people. Since there is no real-world data available, firstly a method is presented to simulate abnormal behaviour that can be observed in daily activity patterns of dementia sufferers. Secondly, Graph Convolutional Networks (GCNs) are exploited to recognise activities based on their granular-level sensor activations. Thirdly, abnormal behaviour related to dementia is detected using activity recognition confidence probabilities. Lastly, GCNs are compared against the state-of-the-art methods. The results obtained indicate that GCNs are able to recognise activities and flag abnormal behaviour related to dementia.

关键词： sensor-based activity recognition Smart homes Abnormal behaviour detection Graph Convolutional Networks Cognitive decline

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Finding Characteristics of Users in sensory Information: From Activities to Personality Traits

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sensorS 2020年第5期20卷 1383-1383页

作者： Lee, Jaeryoung Bastos, Nicholas Chubu Univ Coll Engn Dept Robot Sci & Technol Kasugai Aichi 4878501 Japan

The main objective of this work was to use information provided by a sensor-based activity recognition system to create a profile comprising user habits and link this information to the personality traits of users. User habits are represented by the sequence and duration of often observed daily life activities. based on this information, we represented the user trials (sequence of activities) following a numerical method using Fourier series. The duration and sequence of the activities changed the phase and amplitude of the harmonics present in the Fourier representation. Each trial represented in this manner is called a behavioral spectrum. These data and the scores obtained from personality questionnaires were clustered separately and then an association was created between the clusters. The objective was to associate the activity-related features (sensor-based) and personality traits. The experimental results showed that for both young and elderly subjects, there is an association between the user personality traits and the manner in which they perform their activities. Moreover, the results obtained in this work show a promising method of assessing the personality traits of users based on their activities.

关键词： sensor-based activity recognition sensor-based habit assessment personality traits data clustering

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A Web System for Managing and Monitoring Smart Environments 4th

A Web System for Managing and Monitoring Smart Environments

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4th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO)

作者： Zafra, Daniel Medina, Javier Martinez, Luis Nugent, Chris Espinilla, Macarena Univ Jaen Dept Comp Sci Jaen Spain Univ Ulster Sch Comp & Math Jordanstown BT37 0QB North Ireland

ISBN: (纸本)9783319317441;9783319317434

Smart environments have the ability to record information about the behavior of the people by means of their interactions with the objects within an environment. This kind of environments are providing solutions to address some of the problems associated with the growing size and ageing of the population by means of the recognition of activities, monitoring activities of daily living and adapting the environment. In this contribution, a Web system for managing and monitoring smart environments is introduced as an useful tool to activity recognition. The Web system has the advantages to process the information, accessible services and analytic capabilities. Furthermore, a case study monitored by the proposed Web System is illustrated in order to show its performance, usefulness and effectiveness.

关键词： Smart environments Behavioral detection Monitoring smart environments Managing smart environments sensor-based activity recognition

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Recognizing Multiuser Activities Using Wireless Body sensor Networks

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2011年第11期10卷 1618-1631页

作者： Gu, Tao Wang, Liang Chen, Hanhua Tao, Xianping Lu, Jian Univ So Denmark Dept Math & Comp Sci DK-5230 Odense M Denmark Nanjing Univ Dept Comp Sci State Key Lab Novel Software Technol Nanjing 210093 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Cluster & Grid Comp Lab Serv Comp Technol & Syst Lab Wuhan 430074 Peoples R China

The advances of wireless networking and sensor technology open up an interesting opportunity to infer human activities in a smart home environment. Existing work in this paradigm focuses mainly on recognizing activities of single user. In this work, we focus on the fundamental problem of recognizing activities of multiple users using a wireless body sensor network, and propose a scalable pattern mining approach to recognize both single-and multiuser activities in a unified framework. We exploit Emerging Pattern-a discriminative knowledge pattern which describes significant changes among activity classes of data-for building activity models and design a scalable, noise-resistant, Emerging Pattern-based Multiuser activity Recognizer (epMAR) to recognize both single- and multiuser activities. We develop a multimodal, wireless body sensor network for collecting real-world traces in a smart home environment, and conduct comprehensive empirical studies to evaluate our system. Results show that epMAR outperforms existing schemes in terms of accuracy, scalability, and robustness.

关键词： Wireless body sensor networks sensor-based activity recognition pattern mining

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