Activity recognition plays an important role for pervasive healthcare such as health monitoring, assisted living and pro-active services. Despite of the continuous and transparent sensing with various built-in sensors in mobile devices, activity recognition on mobile devices for pervasive healthcare is still a challenge due to the constraint of resources, such as battery limitation, computation workload, etc. Keeping in view the demand of energy-efficient activity recognition, we propose a hierarchical method to recognize user activities based on a single tri-axial accelerometer in smart phones for health monitoring. Specifically, the contribution of this paper is two-fold. First, it is demonstrated that the activity recognition based on the low sampling frequency is feasible for the long-term activity monitoring. Second, this paper presents a hierarchical recognition scheme. The proposed algorithm reduces the opportunity of usage of time-consuming frequency-domain features and adjusts the size of sliding window to improve recognition accuracy. Experimental results demonstrate the effectiveness of the proposed algorithm, with more than 85 % recognition accuracy rate for 11 activities and 3.2 h extended battery life for mobile phones. Our energy efficient recognition algorithm extends the battery time for activity recognition on mobile devices and contributes to the health monitoring for pervasive healthcare.

Energy-Efficient Motion Related Activity Recognition on Mobile Devices for Pervasive Healthcare

We developed and tested a system for estimating body postures on a bed using unconstrained measurements of electrocardiogram (ECG) signals using 12 capacitively coupled electrodes and a conductive textile sheet. Thirteen healthy subjects participated in the experiment. After detecting the channels in contact with the body among the 12 electrodes, the features were extracted on the basis of the morphology of the QRS (Q wave, R wave, and S wave of ECG) complex using three main steps. The features were applied to linear discriminant analysis, support vector machines with linear and radial basis function (RBF) kernels, and artificial neural networks (one and two layers), respectively. SVM with RBF kernel had the highest performance with an accuracy of 98.4% for estimation of four body postures on the bed: supine, right lateral, prone, and left lateral. Overall, although ECG data were obtained from few sensors in an unconstrained manner, the performance was better than the results that have been reported to date. The developed system and algorithm can be applied to the obstructive apnea detection and analyses of sleep quality or sleep stages, as well as body posture detection for the management of bedsores.

Estimation of Body Postures on Bed Using Unconstrained ECG Measurements

The monitoring of sleep patterns is of major importance for various reasons such as the detection and treatment of sleep disorders, the assessment of the effect of different medical conditions or medications on the sleep quality, and the assessment of mortality risks associated with sleeping patterns in adults and children. Sleep monitoring by itself is a difficult problem due to both privacy and technical considerations. The proposed system uses a combination of non-invasive sensors to assess and report sleep patterns: a contact-based pressure mattress and a non-contact 3D image acquisition device, which can complement each other. To evaluate our system, we used real data collected in Heracleia Lab's assistive living apartment. Our system uses Machine Learning techniques to automatically analyze the collected data and recognize sleep patterns. It is non-invasive, as it does not disrupt the user's usual sleeping behavior and it can be used both at the clinic and at home with minimal cost.

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Non-invasive analysis of sleep patterns via multimodal sensor input

Pressure ulcer is a prevalent complication for bed-bound patients who are not able to shift their body weights over time. Continuous monitoring of patient's postures in the bed can be helpful for caregivers in order to keep track of patient's movements and quality of their repositioning during a day. This information allows hospitals to plan an effective repositioning schedule for each patient. In this paper, a high speed and robust posture classification algorithm is proposed that can be employed in any pervasive patient's monitoring system. First, a whole-body pressure image is recorded using a commercial pressure mat system. Image enhancement is then applied to the raw pressure images and a binary signature for each different posture is constructed. Finally, using a binary pattern matching technique, a given posture can be classified to one of the known posture classes. Our extensive experiments show that the proposed algorithm is able to predict in-bed postures with more than 97% average accuracy.

Continuous eight-posture classification for bed-bound patients

An integrated smart home system (ISHS) is an effective way to improve the quality of life of the elderly The elderly's willingness is essential to adopt an ISHS; to the best of our knowledge, no study has investigated the elderly's perception of ISHS Consequently, this study aims to investigate the elderly's perception of the ISHS by comprehensively evaluating its possible benefits and negative responses A set of sensors required for an ISHS was determined, and interviews were designed based on four factors: perceived comfort, perceived usability, perceived privacy, and perceived benefit Subsequently, technological trials of the sensor-set followed by two focus group interviews were conducted on nine independently living elderly participants at a senior welfare center in South Korea Consistent with previous studies, the results of this investigation indicate that elderly participants elicited negative responses regarding usability complexity, and discomfort to daily activities Despite such negative responses, after acquiring enough awareness about the ISHS's benefits, the elderly acknowledged its necessity and showed a high level of willingness Furthermore, these results indicate that for a better adoption of an ISHS, sufficient awareness regarding its benefits and development of elderly-friendly smart home sensors that minimize negative responses are required

https://www.mdpi.com/1424-8220/21/4/1284/pdf

Elderly Perception on the Internet of Things-Based Integrated Smart-Home System.

Quality of sleep is an important attribute of an elder’s health state and its assessment is still a challenge. The sleep pattern is a significant aspect to evaluate the quality of sleep, and how to recognize elder’s sleep pattern is an important issue for elder-care community. With the pressure sensor matrix to monitor the elder’s sleep behavior in bed, this paper presents an unobtrusive sleep postures detection and pattern recognition approaches. Based on the proposed sleep monitoring system, the processing methods of experimental data and the classification algorithms for sleep pattern recognition are also discussed.

Towards non-intrusive sleep pattern recognition in elder assistive environment

Quality of sleep is an important attribute of an elder's health state and its assessment is still a challenge Sleep pattern is a significant aspect to evaluate the quality of sleep, and how to recognizethe elder's sleep pattern is an importantissuefor elder-care community This paper presents a novel multimodal sensing system to monitor the elder's sleep behavior with the pressure sensor matrix and ultra wide band (UWB) tagsBased on the proposed sleep monitoring system, the paper addresses the unobtrusive sleep postures detection and pattern recognition approaches, and the processing methods of experimental data and theclassification algorithms for sleep pattern recognitionare also discussed

Multi-modal non-intrusive sleep pattern recognition in elder assistive environment

Quality of sleep is an important attribute of an elder's health state and its assessment is still a challenge. The sleep pattern is a significant aspect to evaluate the quality of sleep, and how to recognize elder's sleep pattern is an important issue for elder-care community. With the pressure sensor matrix to monitor the elder's sleep behavior in bed, this paper presents an unobtrusive sleep postures detection and pattern recognition approaches. Based on the proposed sleep monitoring system, the processing methods of experimental data and the classification algorithms for sleep pattern recognition are also discussed.

The quality of sleep may be a reflection of an elderly individual's health state, and sleep pattern is an important measurement. Recognition of sleep pattern by itself is a challenge issue, especially for elderly-care community, due to both privacy concerns and technical limitations. We propose a novelmulti-parametric sensing system called sleep pattern recognition system (SPRS). This system, equipped with a combination of various non-invasive sensors, can monitor an elderly user's sleep behavior. It accumulates the detecting data from a pressure sensor matrix and ultra wide band (UWB) tags. Based on these two types of complementary sensing data, SPRS can assess the user's sleep pattern automatically via machine learning algorithms. Compared to existing systems, SPRS operateswithout disrupting the users' sleep. It can be used in normal households with minimal deployment. Results of tests in our real assistive apartment at the Smart Elder-care Lab are also presented in this paper.

https://www.researchgate.net/profile/Bessam_Abdulrazak/publication/283907488_Non-intrusive_sleep_pattern_recognition_with_ubiquitous_sensing_in_elderly_assistive_environment/links/5658374008ae1ef9297d8e60.pdf

Xingshe Zhou

Papers

Towards non-intrusive sleep pattern recognition in elder assistive environment

Multi-modal non-intrusive sleep pattern recognition in elder assistive environment

Towards non-intrusive sleep pattern recognition in elder assistive environment

Non-intrusive sleep pattern recognition with ubiquitous sensing in elderly assistive environment