A Multi-Sensor Approach for Activity Recognition in Older Patients

in pressInternational audienceExisting surveillance systems for older people activity analysis are focused on video and sensors analysis (e.g., accelerometers, pressure, infrared) applied for frailty assessment, fall detection, and the automatic identification of self-maintenance activities (e.g., dressing, self-feeding) at home. This paper proposes a multi-sensor surveillance system (accelerometers and video-camera) for the automatic detection of instrumental activities of daily living (IADL, e.g., preparing coffee, making a phone call) in a lab-based clinical protocol. IADLs refer to more complex activities than self-maintenance which decline in performance has been highlighted as an indicator of early symptoms of dementia. Ambient video analysis is used to describe older people activity in the scene, and an accelerometer wearable device is used to complement visual information in body posture identification (e.g., standing, sitting). A generic constraint-based ontology language is used to model IADL events using sensors reading and semantic information of the scene (e.g., presence in goal-oriented zones of the environment, temporal relationship of events, estimated postures). The proposed surveillance system is tested with 9 participants (healthy: 4, MCI: 5) in an observation room equipped with home appliances at the Memory Center of Nice Hospital. Experiments are recorded using a 2D video camera (8 fps) and an accelerometer device (MotionPod®). The multi-sensor approach presents an average sensitivity of 93.51% and an average precision of 63.61%, while the vision-based approach has a sensitivity of 77.23%, and a precision of 57.65%. The results show an improvement of the multi-sensor approach over the vision-based at IADL detection. Future work will focus on system use to evaluate the differences between the activity profile of healthy participants and early to mild stage Alzheimer's patients

Video Activity Recognition Framework for assessing motor behavioural disorders in Alzheimer Disease Patients

International audiencePatients with Alzheimers disease show cognitive decline commonly associated with psycho-behavioural disorders like depression, apathy and motor behaviour disturbances. However current evaluations of psycho-behavioural disorders are based on interviews and battery of neuropsychological tests with the presence of a clinician. So these evaluations show limits of subjectivity (e.g., subjective interpretation of clinician at a date t). In this work, we study the ability of a proposed automatic video activity recognition system to detect activity changes between elderly subjects with and without dementia during a clinical experimentation. A total of 28 volunteers (11 healthy elderly subjects, 17 Alzheimer's disease patients (AD)) participate to the experimentation. The proposed study shows that we could differentiate the two profiles of participants based on motor activity parameters, such as the walking speed, computed from the proposed automatic video activity recognition system. These primary results are promising and validating the interest of automatic analysis of video as an objective evaluation tool providing comparative results between participants and over the time

Alzheimer's patient activity assessment using different sensors

International audiencePurpose: Older people population is expected to grow dramatically over the next 20 years (including Alzheimer's patients), while the number of people able to provide care will decrease. We present the development of medical and information and communication technologies to support the diagnosis and evaluation of dementia progress in early stage Alzheimer disease (AD) patients.Method: We compared video and accelerometers activity assessment for the estimation of older people performance in instrumental activities of daily living (IADL) and physical tests in the clinical protocol developed by the Memory Center of the Nice Hospital and the Department of Neurology at National Cheng Kung University Hospital - Taiwan. This clinical protocol defines a set of IADLs (e.g., preparing coffee, watching TV) that could provide objective information about dementia symptoms and be realistically achieved in the two sites observation room. Previous works studied accelerometers activity assessment for the detection of changes in older people gait patterns caused by dementia progress, or video-based event detection for personal self-care activities (ADLs)[1, 2, 3], but none has used both sensors for IADLs analysis. The proposed system uses a constraint-based ontology to model and detect events based on different sensors readings (e.g., 2D video stream data is converted to 3D geometric information that is combined with a priori semantic information, like defined spatial zones or posture estimations given by accelerometer). The ontology language is declarative and intuitive (as it uses natural terminology), allowing medical experts to define and modify the IADL models. The proposed system was tested with 44 participants (healthy=21, AD=23). A stride detection algorithm was developed by the Taiwanese team for the automatic acquisition of patients gait parameters (e.g., stride length, stride frequency) using a tri-axial accelerometer embedded in a wearable device. It was tested with 33 participants (healthy=17, Alzheimer = 16) during a 40 meters walking test. Results & Discussion: The proposed system detected the full set of activities of the first part of our clinical protocol (e.g., repeated transfer test, walking test) with a true positive rate of 96.9 % to 100%. Extracted gait parameters and automatically detected IADLs will be future analyzed for the evaluation of differences between Alzheimer patients at mild to moderate stages and healthy control participants, and for the monitoring of patients motor and cognitive abilities

Detection of activities of daily living impairment in Alzheimer's disease and mild cognitive impairment using information and communication technology

International audienceBackground: One of the key clinical features of Alzheimer's disease (AD) is impairment in daily functioning. Patients with mild cognitive impairment (MCI) also commonly have mild problems performing complex tasks. Information and communication technology (ICT), particularly techniques involving imaging and video processing, is of interest in order to improve assessment. The overall aim of this study is to demonstrate that it is possible using a video monitoring system to obtain a quantifiable assessment of instrumental activities of daily living (IADLs) in AD and in MCI. Methods: The aim of the study is to propose a daily activity scenario (DAS) score that detects functional impairment using ICTs in AD and MCI compared with normal control group (NC). Sixty-four participants over 65 years old were included: 16 AD matched with 10 NC for protocol 1 (P1) and 19 MCI matched with 19 NC for protocol 2 (P2). Each participant was asked to undertake a set of daily tasks in the setting of a "smart home" equipped with two video cameras and everyday objects for use in activities of daily living (8 IADLs for P1 and 11 for P2, plus 4 temporal execution constraints). The DAS score was then computed from quantitative and qualitative parameters collected from video recordings. Results: In P1, the DAS score differentiated AD (DASAD,P1 = 0.47, 95% confidence interval [CI] 0.38-0.56) from NC (DASNC,P1 = 0.71, 95% CI 0.68-0.74). In P2, the DAS score differentiated MCI (DASMCI,P2 = 0.11, 95% CI 0.05-0.16) and NC (DASNC,P2 = 0.36, 95% CI 0.26-0.45). Conclusion: In conclusion, this study outlines the interest of a novel tool coming from the ICT world for the assessment of functional impairment in AD and MCI. The derived DAS scores provide a pragmatic, ecological, objective measurement which may improve the prediction of future dementia, be used as an outcome measurement in clinical trials and lead to earlier therapeutic intervention

The NAMPT Inhibitor FK866 Increases Metformin Sensitivity in Pancreatic Cancer Cells

Pancreatic cancer (pancreatic ductal adenocarcinoma: PDAC) is one of the most aggressive neoplastic diseases. Metformin use has been associated with reduced pancreatic cancer incidence and better survival in diabetics. Metformin has been shown to inhibit PDAC cells growth and survival, both in vitro and in vivo. However, clinical trials using metformin have failed to reduce pancreatic cancer progression in patients, raising important questions about molecular mechanisms that protect tumor cells from the antineoplastic activities of metformin. We confirmed that metformin acts through inhibition of mitochondrial complex I, decreasing the NAD+/NADH ratio, and that NAD+/NADH homeostasis determines metformin sensitivity in several cancer cell lines. Metabolites that can restore the NAD+/NADH ratio caused PDAC cells to be resistant to metformin. In addition, metformin treatment of PDAC cell lines induced a compensatory NAMPT expression, increasing the pool of cellular NAD+. The NAMPT inhibitor FK866 sensitized PDAC cells to the antiproliferative effects of metformin in vitro and decreased the cellular NAD+ pool. Intriguingly, FK866 combined with metformin increased survival in mice bearing KP4 cell line xenografts, but not in mice with PANC-1 cell line xenografts. Transcriptome analysis revealed that the drug combination reactivated genes in the p53 pathway and oxidative stress, providing new insights about the mechanisms leading to cancer cell death