Model and algorithmic framework for detection and correction of cognitive errors

Feki, Mohamed Ali; Biswas, Jit; Tolstikov, Andrei

doi:10.3233/thc-2009-0548

Cited by 11 publications

(5 citation statements)

References 25 publications

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“…The LSTM network was trained based on a certain number of events and tested on either 3000 random events or 10% of the total number of events (for the apartments with very few activity events, e.g. [6][7][8]. This process is repeated three times, and the accuracy values in the graphs correspond to the mean of the best test accuracy of each training.…”

Section: Activity Prediction a Methodsmentioning

confidence: 99%

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Predicting Sensor Events, Activities, and Time of Occurrence Using Binary Sensor Data From Homes With Older Adults

2019

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We present a comprehensive study of state-of-the-art algorithms for the prediction of sensor events and activities of daily living in smart homes. Data have been collected from eight smart homes with real users and 13-17 binary sensors each-including motion, magnetic, and power sensors. We apply two probabilistic methods, namely Sequence Prediction via Enhanced Episode Discovery and Active LeZi, as well as Long Short-Term Memory Recurrent Neural Network, in order to predict the next sensor event in a sequence. We compare these with respect to the required number of preceding sensor events to predict the next, the necessary amount of data to achieve good accuracy and convergence, as well as varying the number of sensors in the dataset. The best-performing method is further improved by including information on the time of occurrence to predict the next sensor event only, and in addition to predict both the next sensor event and the mean time of occurrence in the same model. Subsequently, we apply transfer learning across apartments to investigate its applicability, advantages, and limitations for this setup. Our best implementation achieved an accuracy of 77-87% for predicting the next sensor event, and an accuracy of 73-83% when predicting both the next sensor event and the mean time elapsed to the next sensor event. Finally, we investigate the performance of predicting daily living activities derived from the sensor events. We can predict activities with an accuracy of 61-90%, depending on the apartment. INDEX TERMS Binary sensor, probabilistic method, recurrent neural network, sequence and time prediction, transfer learning.

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Section: Activity Prediction a Methodsmentioning

confidence: 99%

“…A fair amount of research on smart home functions has aimed at assisting older adults with MCI/D in their everyday life [2]. Examples are prompting with reminders or encouragement [3], [4], diagnosis tools [5], [6], as well as prediction, anticipation, and prevention of hazardous situations [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Predicting Sensor Events, Activities, and Time of Occurrence Using Binary Sensor Data From Homes With Older Adults

2019

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“…Audio is not extensively studied for human activity and/or behaviour recognition in healthcare, with only several applications where it was used as a standalone source of information, including emergency event detection [11], [12], and recognition of activities of daily living [13] in the context of elderly care, as well as recognition of suicidal behaviour [14]. In some cases audio was utilised in combination with other information sources (modalities), such as GPS, proximity and activity data for activity/behaviour monitoring of adolescent and young mothers with postpartum depression [15]; or combined with video, ultrasound, temperature, light and infrared sensors for detecting activities of people with mild dementia [16]. More information about these multimodal approaches is provided in section V. A similar approach for the detection of critical emergency events for people with disabilities or elderly people was studied in [12].…”

Section: In Healthcarementioning

confidence: 99%

“…et al[16] propose a theoretical study of a system to assist people affected with mild dementia (an early stage of Alzheimer's Disease) performing their activities of daily living, and also continuously monitor them. In this work, the authors use multiple modalities for data acquisition, including video, audio, ultrasound, temperature, light and infrared sensors with to analyze the cognitive processes underlying executing the actions, detecting errors or inappropriate actions, and providing cues to the user when necessary.…”

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confidence: 99%

Audio- and Video-Based Human Activity Recognition Systems in Healthcare

Cristina,

Despotovic,

Pérez-Rodríguez

et al. 2024

IEEE Access

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In recent years, human activity recognition (HAR) has gained importance in several domains such as surveillance, recognizing indoor and outdoor activities, and providing active and assisted living environments in smart homes and healthcare services. In all these scenarios, audio-, video-and image-based processing algorithms have been applied as well as systems using wearable sensors. This scoping review focuses on audio-and video-based activity recognition systems for healthcare applications. We provide a comprehensive overview of these systems and technologies and discuss their complexity, performance, robustness, stage of development, scalability as well as achievable privacy and security levels. Additionally, we present and discuss datasets that are designed for the evaluation of these activity recognition systems.Although a number of robust approaches have already been proposed, they still pose challenges when it comes to integrating them in larger systems or into clinical practice. We identify challenges for application of audio-and video-based HAR systems in real-word healthcare scenarios, draw recommendations and conclusions based on comparisons of existing approaches, and analyze future trends.INDEX TERMS Human Activity Recognition (HAR), Healthcare, Ambient and Assisted Living (AAL) .

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“…One of goals of smart home development is to provide residents a comfortable and safe living space [1,2]. Smart homes are expected to be able to prompt or warn residents about their health condition [3][4][5][6] by recognizing and forecasting upcoming daily activity [7][8][9][10]. As far as daily activity forecast is concerned, category forecasting and occurrence time forecasting of daily activities are two key tasks.…”

Section: Introductionmentioning

confidence: 99%

A Multi-task Learning Model for Daily Activity Forecast in Smart Home

Yang

Gong

Liu

et al. 2020

Sensors

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Daily activity forecasts play an important role in the daily lives of residents in smart homes. Category forecasts and occurrence time forecasts of daily activity are two key tasks. Category forecasts of daily activity are correlated with occurrence time forecasts, however, existing research has only focused on one of the two tasks. Moreover, the performance of daily activity forecasts is low when the two tasks are performed in series. In this paper, a forecast model based on multi-task learning is proposed to forecast category and occurrence time of daily activity mutually and iteratively. Firstly, raw sensor events are pre-processed to form a feature space of daily activity. Secondly, a parallel multi-task learning model which combines a convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM) units are developed as the forecast model. Finally, five distinct datasets are used to evaluate the proposed model. The experimental results show that compared with the state-of-the-art single-task learning models, this model improves accuracy by at least 2.22%, and the metrics of NMAE, NRMSE and R2 are improved by at least 1.542%, 7.79% and 1.69%, respectively.

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Model and algorithmic framework for detection and correction of cognitive errors

Cited by 11 publications

References 25 publications

Predicting Sensor Events, Activities, and Time of Occurrence Using Binary Sensor Data From Homes With Older Adults

Predicting Sensor Events, Activities, and Time of Occurrence Using Binary Sensor Data From Homes With Older Adults

Audio- and Video-Based Human Activity Recognition Systems in Healthcare

A Multi-task Learning Model for Daily Activity Forecast in Smart Home

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