Detecting the Effect of Alzheimer’s Disease on Everyday Motion Behavior

Kirste, Thomas; Koldrack, Philipp; Schubert, S.; Teipel, Stefan J.

doi:10.1007/978-3-319-11866-6_12

Cited by 13 publications

(17 citation statements)

References 6 publications

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“…However, the controlled lab conditions are exactly the opposite of the daily living conditions where patients are more relaxed, unfocused, and without the anticipation effect of fear of falling [36] or preparation for episodes of disequilibrium. This clearly highlights the importance of cognition, especially attention, on postural balance [12], and the need for complementing lab analysis with kinetic studies on everyday motion behavior [37]. …”

Section: Discussionmentioning

confidence: 99%

Postural Stability Analysis with Inertial Measurement Units in Alzheimer's Disease

Gago¹,

Basto‐Fernandes

Ferreira

et al. 2014

Dement Geriatr Cogn Disord Extra

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Background: The cause of frequent falls in patients with Alzheimer's disease (AD) is still not well understood. Nevertheless, balance control and sensory organization are known to be critical for moving safely and adapting to the environment. Methods: We evaluated postural stability in 20 AD patients (11 fallers and 9 nonfallers) and 16 healthy controls with an inertial measurement unit (triaxial accelerometers and gyroscopes) attached to the center of mass (COM) in different balance conditions (Romberg on flat surface and frontward/backward-inclined surface, with or without visual suppression) in a motor lab. Results: In AD patients, the group of fallers showed a different kinetic pattern of postural stability characterized by higher vulnerability to visual suppression, higher total/maximal displacement and a mediolateral/anteroposterior range of sway, and a consequent need for more corrections of COM pitch and roll angles. Conclusion: Further studies are needed to consolidate the normative values of the discriminatory kinetic variables with the potential of inclusion in a multifactorial analysis of the risk of falls. Nevertheless, these results highlight signs of impairment of central postural control in AD, which may require early therapeutic intervention.

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Section: Discussionmentioning

confidence: 99%

Postural Stability Analysis with Inertial Measurement Units in Alzheimer's Disease

Gago¹,

Basto‐Fernandes

Ferreira

et al. 2014

Dement Geriatr Cogn Disord Extra

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“…The identification of early onsets of dementia using non-medical diagnosis methods requires the development of new diagnostic tools. Although a few promising methods have been experimentally validated [6,7,8,9,10], the translation of the current knowledge into smart homes still requires more dedication and work. Current assessment methods mostly rely on queries from questionnaires or in-person examinations, which depend on recall of events or brief snap-shots of function that may poorly represent a person's typical state of function.…”

Section: Introductionmentioning

confidence: 99%

Detection of abnormal behaviour for dementia sufferers using Convolutional Neural Networks

Arifoǧlu

Bouchachia

2019

Artificial Intelligence in Medicine

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In recent years, there is a rapid increase in the population of elderly people. However, elderly people may suffer from the consequences of cognitive decline, which is a mental health disorder that primarily affects cognitive abilities such as learning, memory, etc. As a result, the elderly people may get dependent on caregivers to complete daily life tasks. Detecting the early indicators of dementia before it gets worsen and warning the caregivers and medical doctors would be helpful for further diagnosis. In this paper, the problem of activity recognition and abnormal behaviour detection is investigated for elderly people with dementia. First of all, the paper presents a methodology for generating synthetic data reflecting on some behavioural difficulties of people with dementia given the difficulty of obtaining real-world data. Secondly, the paper explores Convolutional Neural Networks (CNNs) to model patterns in activity sequences and detect abnormal behaviour related to dementia. Activity recognition is considered as a sequence labelling problem, while abnormal behaviour is flagged based on the deviation from normal patterns. Moreover, the performance of CNNs is compared against the state-of-art methods such as Naïve Bayes (NB), Hidden Markov Models (HMMs), Hidden Semi-Markov Models (HSMM), Conditional Random Fields (CRFs). The results obtained indicate that CNNs are competitive with those state-of-art methods.

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“…The development of ambient home assessment environments has begun to provide the opportunity to assess behaviour change unobtrusively in real-time. Although a few promising methods have been experimentally validated [3]- [5], the translation of the current knowledge into smart homes still requires more dedication and work. Current assessment methods mostly rely on queries from questionnaires or in-person examinations that may poorly represent a person's cognitive status.…”

Section: Introductionmentioning

confidence: 99%

Abnormal Behaviour Detection for Dementia Sufferers via Transfer Learning and Recursive Auto-Encoders

Arifoǧlu

Bouchachia

2019

2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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Cognitive impairment is one of the crucial problems elderly people face. Tracking their daily life activities and detecting early indicators of cognitive decline would be necessary for further diagnosis. Depending on the decline magnitude, monitoring may need to be done over long periods of time to detect abnormal behaviour. In the absence of training data, it would be helpful to learn the normal behaviour and daily life patterns of a (cognitively) healthy person and use them as a basis for tracking other patients. In this paper, we propose to investigate Recursive Auto-Encoders (RAE)-based transfer learning to cope with the problem of scarcity of data in the context of abnormal behaviour detection. We present a method for generating synthetic data to reflect on some behavior of people with dementia. An RAE model is trained on data of a healthy person in a source household. Then, the resulting RAE is used to detect abnormal behavior in a target house. To evaluate the proposed approach, we compare the results with the-state-ofthe-art supervised methods. The results indicate that transfer learning is promising when there is lack of training data.

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Detecting the Effect of Alzheimer’s Disease on Everyday Motion Behavior

Cited by 13 publications

References 6 publications

Postural Stability Analysis with Inertial Measurement Units in Alzheimer's Disease

Postural Stability Analysis with Inertial Measurement Units in Alzheimer's Disease

Detection of abnormal behaviour for dementia sufferers using Convolutional Neural Networks

Abnormal Behaviour Detection for Dementia Sufferers via Transfer Learning and Recursive Auto-Encoders

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