Ballistocardiogram signal processing: a review.

Across the world, healthcare costs are projected to continue to increase, and the pressure on the healthcare system is only going to grow in intensity as the rate of growth of elderly population increases in the coming decades. As an example, when people age one possible condition that they may experience is sleep-disordered breathing (SDB). SDB, better known as the obstructive sleep apnea (OSA) syndrome, and associated cardiovascular complications are among the most common clinical disorders. The gold-standard approach to accurately diagnose OSA, is polysomnography (PSG), a test that should be performed in a specialist sleep clinic and requires a complete overnight stay at the clinic. The PSG system can provide accurate and real-time data; however, it introduces several challenges such as complexity, invasiveness, excessive cost, and absence of privacy. Technological advancements in hardware and software enable noninvasive and unobtrusive sensing of vital signs. An alternative approach which may help diagnose OSA and other cardiovascular diseases is the ballistocardiography. The ballistocardiogram (BCG) signal captures the ballistic forces of the heart caused by the sudden ejection of blood into the great vessels with each heartbeat, breathing, and body movement. In recent years, BCG sensors such as polyvinylidene fluoride film-based sensors, electromechanical films, strain Gauges, hydraulic sensors, microbend fiber-optic sensors as well as fiber Bragg grating sensors have been integrated within ambient locations such as mattresses, pillows, chairs, beds, or even weighing scales, to capture BCG signals, and thereby measure vital signs. Analysis of the BCG signal is a challenging process, despite being a more convenient and comfortable method of vital signs monitoring. In practice, BCG sensors are placed under bed mattresses for sleep tracking, and hence several factors, e.g., mattress thickness, body movements, motion artifacts, bed-partners, etc. can deteriorate the signal. In this paper, we introduce the sensors that are being used for obtaining BCG signals. We also present an in-depth review of the signal processing methods as applied to the various sensors, to analyze the BCG signal and extract physiological parameters such heart rate and breathing rate, as well as determining sleep stages. Besides, we recommend which methods are more suitable for processing BCG signals due to their nonlinear and nonstationary characteristics.

Sensor data quality processing for vital signs with opportunistic ambient sensing.

Opportunistic ambient sensing involves placement of sensors appropriately so that intermittent contact can be made unobtrusively for gathering physiological signals for vital signs. In this paper, we discuss the results of our quality processing system used to extract heart rate from ballisto-cardiogram signals obtained from a micro-bending fiber optic sensor pressure mat. Visual inspection is used to label data into informative and non-informative classes based on their heart rate information. Five classifiers are employed for the classification process, i.e., random forest, support vector machine, multilayer, feedforward neural network, linear discriminant analysis, and decision tree. To compute the overall effectiveness of quality processing, the informative signals are processed to estimate interbeat intervals. The system was used to process, data collected from 50 human subjects sitting in a massage chair while performing different activities. Opportunistically collected data was obtained from the fiber optic sensor mat placed on the headrest of the massage chair. Using our classification approach, 57.37% of the dataset was able to provide informative signals. On the informative signals, random forest classifier achieves the best classification accuracy with a mean accuracy of 98.99%. The average of the mean absolute error between the estimated heart rate and the reference ECG is reduced from 13.2 to 8.47. Therefore, the proposed system shows a good robustness for opportunistic ambient sensing.

Towards numerical temporal-frequency system modelling of associations between electrocardiogram and ballistocardiogram.

Ballistocardiogram (BCG) is a vital sign of ballistic forces generated by each heartbeat. With the advancements in related sensor and computing technologies in recent years, BCG has become far more accessible and thus regained its interest in both research and industry fields. Here we would like to promote the system modelling approach to BCG computing that allows to explore the underlying association between BCG and other physiological signals such as electrocardiogram (ECG). This is in contrast to most of the existing works in the related signal processing domain, which focus on detecting heart rate only. The system modelling approach may eventually improve the clinical significance of the BCG by extracting deeply embedded information. Towards this goal, here we present our preliminary study where we design a Wavelet-based temporal-frequency system model for associating BCG and ECG. To validate the model, we also collect simultaneous BCG and ECG recordings from 4 healthy subjects. We use the system model to build a BCG to ECG predicting algorithm. We demonstrate that this temporal-frequency model and algorithm is far superior, in terms of accuracy, to the naïve method of linear modelling.

An adaptable and flexible framework for assistive living of cognitively impaired people.

On account of chronic neurocognitive disorders, many people progressively lose their autonomy and become more dependent on others, finally reaching the stage when they need round-the-clock care from caregivers. Over time, as patients' needs increase with the evolution of their diseases, caregivers experience increasing levels of stress and burden. Therefore, an assistive solution that is able to adapt to the changing needs of the end-users is needed. This need was considered as a major requirement that emerged from our field work and deployment experience in Singapore. In this paper, we focus on the technical aspects of our deployment, where we were interested in solving the technical requirement of adaptability and extendibility of the framework that has emerged from our predeployment analysis and discussions with professional caregivers. We expose our approach for dynamic integration of assistive services with their related sensing technologies and interaction devices and provide the technical results of the deployment of this solution. We also provide guidelines for real-world deployment of assistive solutions.

Heart rate estimation from FBG sensors using cepstrum analysis and sensor fusion.

This paper presents a method of estimating heart rate from arrays of fiber Bragg grating (FBG) sensors embedded in a mat. A cepstral domain signal analysis technique is proposed to characterize Ballistocardiogram (BCG) signals. With this technique, the average heart beat intervals can be estimated by detecting the dominant peaks in the cepstrum, and the signals of multiple sensors can be fused together to obtain higher signal to noise ratio than each individual sensor. Experiments were conducted with 10 human subjects lying on 2 different postures on a bed. The estimated heart rate from BCG was compared with heart rate ground truth from ECG, and the mean error of estimation obtained is below 1 beat per minute (BPM). The results show that the proposed fusion method can achieve promising heart rate measurement accuracy and robustness against various sensor contact conditions.

Ballistocardiography with fiber optic sensor in headrest position: a feasibility study and a new processing algorithm.

Ballistocardiography (BCG) is a promising unobtrusive method for home e-healthcare systems, and has attracted increasing interest in recent years along with technological advances in related biomedical, electrical engineering and computer science fields. While existing systems have investigated the efficacy of BCG setups in bed, backrest, seat or scale positions, we propose to study BCG in headrest position that will allow new practical and portable applications. To this end, we designed and implemented a multi-modality sensing system including a high-sensitivity microbend fiber optic BCG sensor. In this preliminary study, we have collected multi-modality physiological data on 3 human subjects. We ran extensive analysis on BCG in correlation with ECG, and identified special characteristics of the signal in the new BCG setup. The result suggests that new appropriate computing techniques are necessary for accurately recovering the heart beat signal. Therefore, we developed a novel algorithm for heart beat detection. We evaluate the algorithm with the data and demonstrate that it can accurately compute heart rate intervals in the headrest BCG despite significant signal distortion.

Deployment of assistive living technology in a nursing home environment: methods and lessons learned.

BACKGROUND: With an ever-growing ageing population, dementia is fast becoming the chronic disease of the 21st century. Elderly people affected with dementia progressively lose their autonomy as they encounter problems in their Activities of Daily Living (ADLs). Hence, they need supervision and assistance from their family members or professional caregivers, which can often lead to underestimated psychological and financial stress for all parties. The use of Ambient Assistive Living (AAL) technologies aims to empower people with dementia and relieve the burden of their caregivers.The aim of this paper is to present the approach we have adopted to develop and deploy a system for ambient assistive living in an operating nursing home, and evaluate its performance and usability in real conditions. Based on this approach, we emphasise on the importance of deployments in real world settings as opposed to prototype testing in laboratories. METHODS: We chose to conduct this work in close partnership with end-users (dementia patients) and specialists in dementia care (professional caregivers). Our trial was conducted during a period of 14 months within three rooms in a nursing home in Singapore, and with the participation of eight dementia patients and two caregivers. A technical ambient assistive living solution, consisting of a set of sensors and devices controlled by a software platform, was deployed in the collaborating nursing home. The trial was preceded by a pre-deployment period to organise several observation sessions with dementia patients and focus group discussions with professional caregivers. A process of ground truth and system's log data gathering was also planned prior to the trial and a system performance evaluation was realised during the deployment period with the help of caregivers. An ethical approval was obtained prior to real life deployment of our solution. RESULTS: Patients' observations and discussions allowed us to gather a set of requirements that a system for elders with mild-dementia should fulfil. In fact, our deployment has exposed more concrete requirements and problems that need to be addressed, and which cannot be identified in laboratory testing. Issues that were neither forecasted during the design phase nor during the laboratory testing surfaced during deployment, thus affecting the effectiveness of the proposed solution. Results of the system performance evaluation show the evolution of system precision and uptime over the deployment phases, while data analysis demonstrates the ability to provide early detection of the degradation of patients' conditions. A qualitative feedback was collected from caregivers and doctors and a set of lessons learned emerged from this deployment experience. CONCLUSION: Lessons learned from this study were very useful for our research work and can serve as inspiration for developers and providers of assistive living services. They confirmed the importance of real deployment to evaluate assistive solutions especially with the involvement of professional caregivers. They also asserted the need for larger deployments. Larger deployments will allow to conduct surveys on assistive solutions social and health impact, even though they are time and manpower consuming during their first phases.

Activity recognition using correlated pattern mining for people with dementia.

Due to the rapidly aging population around the world, senile dementia is growing into a prominent problem in many societies. To monitor the elderly dementia patients so as to assist them in carrying out their basic Activities of Daily Living (ADLs) independently, sensors are deployed in their homes. The sensors generate a stream of context information, i.e., snippets of the patient's current happenings, and pattern mining techniques can be applied to recognize the patient's activities based on these micro contexts. Most mining techniques aim to discover frequent patterns that correspond to certain activities. However, frequent patterns can be poor representations of activities. In this paper, instead of using frequent patterns, we propose using correlated patterns to represent activities. Using simulation data collected in a smart home testbed, our experimental results show that using correlated patterns rather than frequent ones improves the recognition performance by 35.5% on average.

Processing of wearable sensor data on the cloud - a step towards scaling of continuous monitoring of health and well-being.

As part of a sleep monitoring project, we used actigraphy based on body-worn accelerometer sensors to remotely monitor and study the sleep-wake cycle of elderly staying at nursing homes. We have conducted a fifteen patient trial of a sleep activity pattern monitoring (SAPM) system at a local nursing home. The data was collected and stored in our server and the processing of the data was done offline after sleep diaries used for validation and ground truth were updated into the system. The processing algorithm matches and annotates the sensor data with manual sleep diary information and is processed asynchronously on the grid/cloud back end. In this paper we outline the mapping of the system for grid / cloud processing, and initial results that show expected near-linear performance for scaling the number of users.

Evaluation and analysis of multimodal sensors for developing in and around the bed patient monitoring system.

Due to the decline in physical and cognitive abilities, many frail elderly may have to lie in the bed most of their time. It is not feasible to monitor them continuously through manual observations alone. This issue can be resolved by embedding a set of multimodal sensors into the bed and providing automated activity recognition intelligence. But it is important to design and develop such multimodal sensing intelligence system desirable to the demands made by the clinicians. This paper presents the comparison and evaluation of different sensing bed configurations to observe different granularities of patient's contexts and activities in and around the bed. Based on the achievements and lessons learned from the experimental analysis, we propose improved sensing bed hardware and software systems to meet the real needs of in and around the bed patient monitoring.

Sleeping patterns observation for bedsores and bed-side falls prevention.

Disabled or cognition impaired elderly may lie in the bed most of their time. It is important to monitor their health conditions and look out for life threatening events in and around the bed continuously. Abrupt unassisted movements may lead to falls whereas the lack of desirable movements may cause bedsores. In order to alleviate these problems, we propose automated means of continuous and unobtrusive sleeping pattern observation through pressure sensing bed. By understanding of subjects' states from observed pressure evidences, timely intervention and nursing care can be provided to subjects immediately. This enables provision of high quality care to frail and dependent elderly, and also enhances their quality of life in a cost-effective and resource-efficient manner.

Model and algorithmic framework for detection and correction of cognitive errors.

This paper outlines an approach that we are taking for elder-care applications in the smart home, involving cognitive errors and their compensation. Our approach involves high level modeling of daily activities of the elderly by breaking down these activities into smaller units, which can then be automatically recognized at a low level by collections of sensors placed in the homes of the elderly. This separation allows us to employ plan recognition algorithms and systems at a high level, while developing stand-alone activity recognition algorithms and systems at a low level. It also allows the mixing and matching of multi-modality sensors of various kinds that go to support the same high level requirement. Currently our plan recognition algorithms are still at a conceptual stage, whereas a number of low level activity recognition algorithms and systems have been developed. Herein we present our model for plan recognition, providing a brief survey of the background literature. We also present some concrete results that we have achieved for activity recognition, emphasizing how these results are incorporated into the overall plan recognition system.

Smart wireless continence management system for persons with dementia.

Incontinence is highly prevalent in the elderly population, especially in nursing home residents with dementia. It is a distressing and costly health problem that affects not only the patients but also the caregivers. Effective continence management is required to provide quality care, and to eliminate high labor costs and annoyances to the caregivers resulting from episodes of incontinence. This paper presents the design, development, and preliminary deployment of a smart wireless continence management system for dementia-impaired elderly or patients in institutional care settings such as nursing homes and hospitals. Specifically, the mote wireless platform was used to support the deployment of potentially large quantities of wetness sensors with wider coverage and with dramatically less complexity and cost. It consists of an intelligent signal relay mechanism so that the residents are free to move about in the nursing home or hospital and allows personalized continence management service. Preliminary results from a trial in a local nursing home are promising and can significantly improve the quality of care for patients.