Real-Time Detection of Spatial Disorientation in Persons with Mild Cognitive Impairment and Dementia.

BACKGROUND: Detecting manifestations of spatial disorientation in real time is a key requirement for adaptive assistive navigation systems for people with dementia. OBJECTIVE: To identify predictive patterns of spatial disorientation in cognitively impaired people during unconstrained locomotion behavior in an urban environment. METHODS: Accelerometric data and GPS records were gathered during a wayfinding task along a route of about 1 km in 15 people with amnestic mild cognitive impairment or clinically probable Alzheimer's disease dementia (13 completers). We calculated a set of 48 statistical features for each 10-s segment of the acceleration sensor signal to characterize the physical motion. We used different classifiers with the wrapper method and leave-one-out cross-validation for feature selection and for determining accuracy of disorientation detection. RESULTS: Linear discriminant analysis using three features showed the best classification results, with a cross-validated ROC AUC of 0.75, detecting 65% of all scenes of spatial disorientation in real time. Consideration of an additional feature that informed about a person's distance to the next traffic junction did not provide an additional information gain. CONCLUSIONS: Accelerometric data are able to capture the uniformity and activity of a person's walking, which are identified as the most informative locomotion features of spatially disoriented behavior. This serves as an important basis for real-time navigation assistance. To improve the required accuracy of real-time disorientation prediction, as a next step we will analyze whether location-based behavior is able to inform about person-centered habitual factors of orientation.

Situation Model for Situation-Aware Assistance of Dementia Patients in Outdoor Mobility.

BACKGROUND: Dementia impairs spatial orientation and route planning, thus often affecting the patient's ability to move outdoors and maintain social activities. Situation-aware deliberative assistive technology devices (ATD) can substitute impaired cognitive function in order to maintain one's level of social activity. To build such a system, one needs domain knowledge about the patient's situation and needs. We call this collection of knowledge situation model. OBJECTIVE: To construct a situation model for the outdoor mobility of people with dementia (PwD). The model serves two purposes: 1) as a knowledge base from which to build an ATD describing the mobility of PwD; and 2) as a codebook for the annotation of the recorded behavior. METHODS: We perform systematic knowledge elicitation to obtain the relevant knowledge. The OBO Edit tool is used for implementing and validating the situation model. The model is evaluated by using it as a codebook for annotating the behavior of PwD during a mobility study and interrater agreement is computed. In addition, clinical experts perform manual evaluation and curation of the model. RESULTS: The situation model consists of 101 concepts with 11 relation types between them. The results from the annotation showed substantial overlapping between two annotators (Cohen's kappa of 0.61). CONCLUSION: The situation model is a first attempt to systematically collect and organize information related to the outdoor mobility of PwD for the purposes of situation-aware assistance. The model is the base for building an ATD able to provide situation-aware assistance and to potentially improve the quality of life of PwD.

Detecting the effect of Alzheimer's disease on everyday motion behavior.

BACKGROUND: Early detection of behavioral changes in Alzheimer's disease (AD) would help the design and implementation of specific interventions. OBJECTIVE: The target of our investigation was to establish a correlation between diagnosis and unconstrained motion behavior in subjects without major clinical behavior impairments. METHOD: We studied everyday motion behavior in 23 dyads with one partner suffering from AD dementia and one cognitively healthy partner in the subjects' home, employing ankle-mounted three-axes accelerometric sensors. We determined frequency features obtained from the signal envelopes computed by an envelope detector for the carrier band 0.5 Hz to 5 Hz. Based on these features, we employed quadratic discriminant analysis for building models discriminating between AD patients and healthy controls. RESULTS: After leave-one-out cross-validation, the classification accuracy of motion features reached 91% and was superior to the classification accuracy based on the Cohen-Mansfield Agitation Inventory (CMAI). Motion features were significantly correlated with MMSE and CMAI scores. CONCLUSION: Our findings suggest that changes of everyday behavior are detectable in accelerometric behavior protocols even in the absence of major clinical behavioral impairments in AD.