Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry

Early detection of high fall risk is an essential component of fall prevention in older adults. Wearable sensors can provide valuable insight into daily-life activities; biomechanical features extracted from such inertial data have been shown to be of added value for the assessment of fall risk. Body-worn sensors such as accelerometers can provide valuable insight into fall risk. Currently, biomechanical features derived from accelerometer data are used for the assessment of fall risk. Here, we studied whether deep learning methods from machine learning are suited to automatically derive features from raw accelerometer data that assess fall risk. We used an existing dataset of 296 older adults. We compared the performance of three deep learning model architectures (convolutional neural network (CNN), long short-term memory (LSTM) and a combination of these two (ConvLSTM)) to each other and to a baseline model with biomechanical features on the same dataset. The results show that the deep learning models in a single-task learning mode are strong in recognition of identity of the subject, but that these models only slightly outperform the baseline method on fall risk assessment. When using multi-task learning, with gender and age as auxiliary tasks, deep learning models perform better. We also found that preprocessing of the data resulted in the best performance (AUC = 0.75). We conclude that deep learning models, and in particular multi-task learning, effectively assess fall risk on the basis of wearable sensor data

Unsupervised visit detection in smart homes

Assistive technologies for elderly often use ambient sensor systems to infer activities of daily living (ADL). In general such systems assume that only a single person (the resident) is present in the home. However, in real world environments, it is common to have visits and it is crucial to know when the resident is alone or not. We deal with this challenge by presenting a novel method that models regular activity patterns and detects visits. Our method is based on the Markov modulated Poisson process (MMPP), but is extended to allow the incorporation of multiple feature streams. The results from the experiments on nine months of sensor data collected in two apartments show that our model significantly outperforms the standard MMPP. We validate the generalisation of the model using two new data sets collected from an other sensor network

Measuring Regularity in Daily Behavior for the Purpose of Detecting Alzheimer

This paper presents a study of sensor data from a person who developed Alzheimer’s disease during a 4-year monitoring period and who is monitored with simple ambient sensors in her home. Our aim is to find data analysis methods that reveal relevant changes in the sensor pattern that occur before the diagnosis. We focus on the quantification of regularity, which is identified as a relevant indicator for the assessment of a disease such as Alzheimer’s. Two unsupervised methods are studied. Restricted Boltzmann Machines are trained and the resulting weights are visualized to see whether there are changes in regularity in the behavioral pattern. Fast Fourier Transformation is applied to the sensor data and the spectral characteristics are determined and compared with the same purpose. Both methods reveal changes in the pattern between different periods. Both methods therefore are useful in quantifying and understanding changes in the regularity of the daily pattern

Unsupervised visit detection in smart homes

Assistive technologies for elderly often use ambient sensor systems to infer activities of daily living (ADL). In general such systems assume that only a single person (the resident) is present in the home. However, in real world environments, it is common to have visits and it is crucial to know when the resident is alone or not. We deal with this challenge by presenting a novel method that models regular activity patterns and detects visits. Our method is based on the Markov modulated Poisson process (MMPP), but is extended to allow the incorporation of multiple feature streams. The results from the experiments on nine months of sensor data collected in two apartments show that our model significantly outperforms the standard MMPP. We validate the generalisation of the model using two new data sets collected from an other sensor network

Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry

Early detection of high fall risk is an essential component of fall prevention in older adults. Wearable sensors can provide valuable insight into daily-life activities; biomechanical features extracted from such inertial data have been shown to be of added value for the assessment of fall risk. Body-worn sensors such as accelerometers can provide valuable insight into fall risk. Currently, biomechanical features derived from accelerometer data are used for the assessment of fall risk. Here, we studied whether deep learning methods from machine learning are suited to automatically derive features from raw accelerometer data that assess fall risk. We used an existing dataset of 296 older adults. We compared the performance of three deep learning model architectures (convolutional neural network (CNN), long short-term memory (LSTM) and a combination of these two (ConvLSTM)) to each other and to a baseline model with biomechanical features on the same dataset. The results show that the deep learning models in a single-task learning mode are strong in recognition of identity of the subject, but that these models only slightly outperform the baseline method on fall risk assessment. When using multi-task learning, with gender and age as auxiliary tasks, deep learning models perform better. We also found that preprocessing of the data resulted in the best performance (AUC = 0.75). We conclude that deep learning models, and in particular multi-task learning, effectively assess fall risk on the basis of wearable sensor data

Continuous Gait Velocity Analysis Using Ambient Sensors in a Smart Home

We present a method for measuring gait velocity using data from an existing ambient sensor network. Gait velocity is an important predictor of fall risk and functional health. In contrast to other approaches that use specific sensors or sensor configurations our method imposes no constraints on the elderly. We studied different probabilistic models for the description of the sensor patterns. Experiments are carried out on 15 months of data and include repeated assessments from an occupational therapist. We showed that the measured gait velocities correlate with these assessments

Een toolkit voor onderzoeksmethoden in het HBO

Welke methoden en technieken zijn er voorhanden voor onderzoek in de ICT beroepspraktijk? Vanuit een gezamenlijke behoefte aan overzicht van methoden en technieken die geschikt zijn voor praktijkonderzoek in de ICT heeft een HBO-i werkgroep bestaande uit vertegenwoordigers van negen hogescholen een digitale toolkit samengesteld. Om overzicht over de methoden en technieken te bieden zijn de methodenkaart praktijkonderzoek en de fase van methodisch werken als organisatorische principes gebruikt. De toolkit biedt docenten en studenten de mogelijkheid geschikte onderzoeksmethoden te selecteren en gebruiken. De toolkit heeft een wiki-formaat, waardoor docenten deze kunnen updaten en onderhouden. In deze bijdrage nodigen we collega-docenten uit om actief te gaan bijdragen; te beginnen in de sessie zelf

Onderwijs in onderzoek, hoe doen we dat, modellen, keuzes en best practices

Samenvatting van de auteurs: De ambitie om onderzoek een goede plaats te geven in het onderwijs van ICT studenten in het HBO, was de aanleiding voor vertegenwoordigers van negen hogescholen om ervaringen en inzichten uit te wisselen. Daarmee is in beeld gekomen welke interpretaties van onderzoek gehanteerd worden, de manier waarop dit in curricula verwerkt is en welke ontwikkelingen daarin bestaan. Onderzoek in het HBO begint zich daarbij steeds duidelijker met een eigen identiteit af te tekenen. In deze bijdrage schetsen we aan de hand van concrete voorbeelden welke keuzes gemaakt zijn, welke knelpunten geconstateerd worden en welke ontwikkeling er plaatsvinden. Bij de deelnemers toetsen we de herkenbaarheid van onze waarnemingen