Unobtrusive Sensors Research Articles

Feasibility of Fall-Risk Detection in Older Adults: Real-World Use of Sensor Data With Machine Learning.

To use machine learning techniques with sensor data to predict fall risk in older adults aging in place. We tested the feasibility of using anomaly detection on a dataset comprising 315 days of continuous unobtrusive sensor data obtained from a single participant to predict fall risk within a 10-day window. Predictions were validated with performance metrics, including accuracy, F1 score, and receiver operating characteristic-area under curve (ROC-AUC), using actual falls documented in the electronic health record. The model resulted with accuracy = 0.96 (95% confidence interval [CI] [0.94, 0.99]), F1 = 0.78 (95% CI [0.73, 0.83]), and ROC-AUC = 0.89 (95% CI [0.85, 0.93]). The application of anomaly detection on sensor data may provide a timely and valid indication of fall risk in older adults within a 10-day window. Further research and validation are warranted to confirm these findings and expand the scope of application in the domain of older adult care and health care support. [Journal of Gerontological Nursing, 50(10), 7-10.].

Unobtrusive Monitoring of Clinical Deterioration in Smart Homes.

Clinical deterioration (CD) is the physiological decompensation that incurs care escalation, protracted hospital stays, or even death. The early warning score (EWS) calculates the occurrence of CD based on five vital signs. However, there are limited reports regarding EWS monitoring in smart home settings. This study aims to design a CD detection system for health monitoring at home (HM@H) that automatically identifies unstable vital signs and alarms the medical emergency team. We conduct a requirement analysis by interviewing experts. We use unified modeling language (UML) diagrams to define the behavioral and structural aspects of HM@H. We developed a prototype using a SQL-based database and Python to calculate the EWS in the front end. A team of five experts assessed the accuracy and validity of the designed system. The requirement analysis for four main users yielded 30 data elements and 10 functions. Three main components of HM@H are the graphical user interface (GUI), the application programming interface (API), and the server. Results show the possibility of using unobtrusive sensors to collect smart home residents' vital signs and calculate their EWS scores in real-time. However, further implementation with real data, for frail elderly and hospital-discharged patients is required.

Estimating the Severity of Obstructive Sleep Apnea Using ECG, Respiratory Effort and Neural Networks.

wearable sensor technology has progressed significantly in the last decade, but its clinical usability for the assessment of obstructive sleep apnea (OSA) is limited by the lack of large and representative datasets simultaneously acquired with polysomnography (PSG). The objective of this study was to explore the use of cardiorespiratory signals common in standard PSGs which can be easily measured with wearable sensors, to estimate the severity of OSA. an artificial neural network was developed for detecting sleep disordered breathing events using electrocardiogram (ECG) and respiratory effort. The network was combined with a previously developed cardiorespiratory sleep staging algorithm and evaluated in terms of sleep staging classification performance, apnea-hypopnea index (AHI) estimation, and OSA severity estimation against PSG on a cohort of 653 participants with a wide range of OSA severity. four-class sleep staging achieved a κ of 0.69 versus PSG, distinguishing wake, combined N1-N2, N3 and REM. AHI estimation achieved an intraclass correlation coefficient of 0.91, and high diagnostic performance for different OSA severity thresholds. this study highlights the potential of using cardiorespiratory signals to estimate OSA severity, even without the need for airflow or oxygen saturation (SpO2), traditionally used for assessing OSA. while further research is required to translate these findings to practical and unobtrusive sensors, this study demonstrates how existing, large datasets can serve as a foundation for wearable systems for OSA monitoring. Ultimately, this approach could enable long-term assessment of sleep disordered breathing, facilitating new avenues for clinical research in this field.

A multi-task learning model using RR intervals and respiratory effort to assess sleep disordered breathing

BackgroundSleep-disordered breathing (SDB) affects a significant portion of the population. As such, there is a need for accessible and affordable assessment methods for diagnosis but also case-finding and long-term follow-up. Research has focused on exploiting cardiac and respiratory signals to extract proxy measures for sleep combined with SDB event detection. We introduce a novel multi-task model combining cardiac activity and respiratory effort to perform sleep–wake classification and SDB event detection in order to automatically estimate the apnea–hypopnea index (AHI) as severity indicator.MethodsThe proposed multi-task model utilized both convolutional and recurrent neural networks and was formed by a shared part for common feature extraction, a task-specific part for sleep–wake classification, and a task-specific part for SDB event detection. The model was trained with RR intervals derived from electrocardiogram and respiratory effort signals. To assess performance, overnight polysomnography (PSG) recordings from 198 patients with varying degree of SDB were included, with manually annotated sleep stages and SDB events.ResultsWe achieved a Cohen’s kappa of 0.70 in the sleep–wake classification task, corresponding to a Spearman’s correlation coefficient (R) of 0.830 between the estimated total sleep time (TST) and the TST obtained from PSG-based sleep scoring. Combining the sleep–wake classification and SDB detection results of the multi-task model, we obtained an R of 0.891 between the estimated and the reference AHI. For severity classification of SBD groups based on AHI, a Cohen’s kappa of 0.58 was achieved. The multi-task model performed better than a single-task model proposed in a previous study for AHI estimation, in particular for patients with a lower sleep efficiency (R of 0.861 with the multi-task model and R of 0.746 with single-task model with subjects having sleep efficiency < 60%).ConclusionAssisted with automatic sleep–wake classification, our multi-task model demonstrated proficiency in estimating AHI and assessing SDB severity based on AHI in a fully automatic manner using RR intervals and respiratory effort. This shows the potential for improving SDB screening with unobtrusive sensors also for subjects with low sleep efficiency without adding additional sensors for sleep–wake detection.

Neuro-adaptive architecture: Buildings and city design that respond to human emotions, cognitive states

Neuro-adaptive architecture has emerged as an interdisciplinary field aiming to cultivate buildings and urban environments responsive to human emotions, cognition, and well-being. Technological advances now enable unprecedented monitoring of occupants' psychological states through unobtrusive sensors, as well as adaptive modulation of environments via “smart” architectural components. If developed responsibly, these advancements hold great potential to optimize human experience and flourishing within the built milieu. However, they also present complex ethical challenges regarding privacy, consent, data security, globalization and equitable access that require thoughtful consideration. This paper provides a comprehensive review and synthesis of the opportunities and dilemmas at the nexus of neuroscience, architecture, and urban planning. Drawing from research worldwide, it examines the multidimensional issues involved and strategies for addressing them through participatory and empathic design practices. Case studies of experimental neuro-adaptive projects are discussed and recommendations provided for longitudinal evaluation of impacts on health, social outcomes, and well-being. Concepts such as cognitive ergonomics, sensory perception and emotional design, restorative urbanism, and adaptive living interfaces are explored through diverse methodologies and design hypotheses are provided for future interdisciplinary collaboration. Overall, this paper argues that responsibly optimized neuro-adaptive architecture could enhance human thriving in complex urban environments, but precautions are necessary to avoid risks to autonomy, equity or unintended consequences. Continued rigorous interdisciplinary work is imperative to navigate these opportunities and challenges, with consideration of technical, social and ethical implications at individual and societal levels.

Unobtrusive Sensors for Synchronous Monitoring of Different Breathing Parameters in Care Environments.

Respiratory problems are common amongst older people. The rapid increase in the ageing population has led to a need for developing technologies that can monitor such conditions unobtrusively. This paper presents a novel study that investigates Wi-Fi and ultra-wideband (UWB) antenna sensors to simultaneously monitor two different breathing parameters: respiratory rate, and exhaled breath. Experiments were carried out with two subjects undergoing three breathing cases in breaths per minute (BPM): (1) slow breathing (12 BPM), (2) moderate breathing (20 BPM), and (3) fast breathing (28 BPM). Respiratory rates were captured by Wi-Fi sensors, and the data were processed to extract the respiration rates and compared with a metronome that controlled the subjects' breathing. On the other hand, exhaled breath data were captured by a UWB antenna using a vector network analyser (VNA). Corresponding reflection coefficient data (S11) were obtained from the subjects at the time of exhalation and compared with S11 in free space. The exhaled breath data from the UWB antenna were compared with relative humidity, which was measured with a digital psychrometer during the breathing exercises to determine whether a correlation existed between the exhaled breath's water vapour content and recorded S11 data. Finally, captured respiratory rate and exhaled breath data from the antenna sensors were compared to determine whether a correlation existed between the two parameters. The results showed that the antenna sensors were capable of capturing both parameters simultaneously. However, it was found that the two parameters were uncorrelated and independent of one another.

Transferability of a Sensing Mattress for Posture Classification from Research into Clinics.

Sleep is crucial in rehabilitation processes, promoting neural plasticity and immune functions. Nocturnal body postures can indicate sleep quality and frequent repositioning is required to prevent bedsores for bedridden patients after a stroke or spinal cord injury. Polysomnography (PSG) is considered the gold standard for sleep assessment. Unobtrusive methods for classifying sleep body postures have been presented with similar accuracy to PSG, but most evaluations have been done in research lab environments. To investigate the challenges in the usability of a previously validated device in a clinical setting, we recorded the sleep posture of 17 patients with a sensorized mattress. Ground-truth labels were collected automatically from a PSG device. In addition, we manually labeled the body postures using video data. This allowed us also to evaluate the quality of the PSG labels. We trained neural networks based on the VGG-3 architecture to classify lying postures and used a self-label correction method to account for noisy labels in the training data. The models trained with the video labels achieved a higher classification accuracy than those trained with the PSG labels (0.79 vs. 0.68). The self-label correction could further increase the models' scores based on video and PSG labels to 0.80 and 0.70, respectively. Unobtrusive sensors validated in clinics can, therefore, potentially improve the quality of care for bedridden patients and advance the field of rehabilitation.

Recruitment and Retention of Recreational Runners in Prospective Injury Research: A Qualitative Study

Purpose Continuous and long-term prospective monitoring of athletes in natural training environments is essential to provide further clarity on the risk factors for running-related injuries. However, participant recruitment and retention can be problematic. This study aimed to identify factors for facilitating the recruitment and retention of recreational runners in prospective, longitudinal running-related injury research involving running technologies. Methods Twenty-seven recreational runners (14 female, 13 male) participated across nine semi-structured focus groups. Focus groups were audio and video recorded and transcribed verbatim. A reflexive thematic analysis was undertaken, with a critical friend approach taken to enhance reliability. Results Incentives, recruiting suitable participants, ease of use of running technologies, an appropriate research design, and good communication practices will facilitate recruitment and retention. Conclusion Receiving study outputs, evidence-based information and undergoing laboratory testing were identified as incentives, however, researchers need to consider whether these may influence participant behaviour and adversely bias the findings of their study. Researchers should offer participants an option with regard to the type, content, frequency and mode of delivery of incentives and communication. Appealing to potential participants’ personal interests will facilitate initial recruitment, while attempts to ‘feed’ this interest throughout the course of a study will enhance retention. Employing a user-friendly smartphone app and unobtrusive sensor(s), and a research study that can work with runners’ training schedules and technology usage habits, will further facilitate their recruitment and retention.

Remote Digital Technologies for the Early Detection and Monitoring of Cognitive Decline in Patients With Type 2 Diabetes: Insights From Studies of Neurodegenerative Diseases.

Type 2 diabetes (T2D) is a risk factor for cognitive decline. In neurodegenerative disease research, remote digital cognitive assessments and unobtrusive sensors are gaining traction for their potential to improve early detection and monitoring of cognitive impairment. Given the high prevalence of cognitive impairments in T2D, these digital tools are highly relevant. Further research incorporating remote digital biomarkers of cognition, behavior, and motor functioning may enable comprehensive characterizations of patients with T2D and may ultimately improve clinical care and equitable access to research participation. The aim of this commentary article is to review the feasibility, validity, and limitations of using remote digital cognitive tests and unobtrusive detection methods to identify and monitor cognitive decline in neurodegenerative conditions and apply these insights to patients with T2D.

Vitality surveillance at distance using thin-film tandem-like narrowband near-infrared photodiodes with light-enhanced responsivity

Remote measurement of vital sign parameters like heartbeat and respiration rate represents a compelling challenge in monitoring an individual's health in a noninvasive way. This could be achieved by large field-of-view, easy-to-integrate unobtrusive sensors, such as large-area thin-film photodiodes. At long distances, however, discriminating weak light signals from background disturbance demands superior near-infrared (NIR) sensitivity and optical noise tolerance. Here, we report an inherently narrowband solution-processed, thin-film photodiode with ultrahigh and controllable NIR responsivity based on a tandem-like perovskite-organic architecture. The device has low dark currents (<10-6 mA cm-2), linear dynamic range >150 dB, and operational stability over time (>8hours). With a narrowband quantum efficiency that can exceed 200% at 850 nm and intrinsic filtering of other wavelengths to limit optical noise, the device exhibits higher tolerance to background light than optically filtered silicon-based sensors. We demonstrate its potential in remote monitoring by measuring the heart rate and respiration rate from distances up to 130 cm in reflection.

SMART HOME DATA VISUALIZATION FOR PROACTIVE HEALTH MONITORING OF COMMUNITY DWELLING OLDER ADULTS

Abstract The role of Ambient Assistive Living and smart home technologies, which utilize unobtrusive sensors to detect changes in health, is becoming increasingly important in the delivery of healthcare services to older adults. However, these technologies must be designed to meaningfully incorporate into clinicians’ decision making. Research has shown when clinicians are engaged in the design process of smart home systems, the accuracy and efficacy of the systems are improved. We present the process undertaken by a team of nurse researchers and computer science engineers to design clinically meaningful behavior markers derived from smart home sensor data that can be used by nurses to proactively identify changes in patient status. During the first phase of design, nurse researchers qualitatively analyzed time series from smart home sensors installed in the homes of community dwelling older adults and identified patterns in these data related to significant health changes. From this analysis, we assembled a candidate list of 15 sensor-based behavior metrics, such as percent time spent in each room or frequency of bathroom use. During the second phase of design, we will build on lessons we learned from participatory design to create behavior markers and visualizations that are inspired by clinical experience. These include visualizing behavior change over time, highlighting behavioral anomalies at multiple time scales, and calculating markers that are not directly observable such as time spent out of home. Lessons learned from clinicians using the data visualizations to proactively screen for health changes in near real time will also be discussed.

Sensor-Based Motion Tracking System Evaluation for RULA in Assembly Task.

Industries need a mechanism to monitor the workers' safety and to prevent Work-related Musculoskeletal Disorders (WMSDs). The development of ergonomics assessment tools helps the industry evaluate workplace design and worker posture. Many studies proposed the automated ergonomics assessment method to replace the manual; however, it only focused on calculating body angle and assessing the wrist section manually. This study aims to (a) propose a wrist kinematics measurement based on unobtrusive sensors, (b) detect potential WMSDs related to wrist posture, and (c) compare the wrist posture of subjects while performing assembly tasks to achieve a comprehensive and personalized ergonomic assessment. The wrist posture measurement is combined with the body posture measurement to provide a comprehensive ergonomics assessment based on RULA. Data were collected from subjects who performed the assembly process to evaluate our method. We compared the risk score assessed by the ergonomist and the risk score generated by our method. All body segments achieved more than an 80% similarity score, enhancing the scores for wrist position and wrist twist by 6.8% and 0.3%, respectively. A hypothesis analysis was conducted to evaluate the difference across the subjects. The results indicate that every subject performs tasks differently and has different potential risks regarding wrist posture.

Stressors and Destressors in Working From Home Based on Context and Physiology From Self-Reports and Smartwatch Measurements: International Observational Study Trial.

The COVID-19 pandemic has greatly boosted working from home as a way of working, which is likely to continue for most companies in the future, either in fully remote or in hybrid form. To manage stress levels in employees working from home, insights into the stressors and destressors in a home office first need to be studied. We present an international remote study with employees working from home by making use of state-of-the-art technology (ie, smartwatches and questionnaires through smartphones) first to determine stressors and destressors in people working from home and second to identify smartwatch measurements that could represent these stressors and destressors. Employees working from home from 3 regions of the world (the United States, the United Kingdom, and Hong Kong) were asked to wear a smartwatch continuously for 7 days and fill in 5 questionnaires each day and 2 additional questionnaires before and after the measurement week. The entire study was conducted remotely. Univariate statistical analyses comparing variable distributions between low and high stress levels were followed by multivariate analysis using logistic regression, considering multicollinearity by using variance inflation factor (VIF) filtering. A total of 202 people participated, with 198 (98%) participants finishing the experiment. Stressors found were other people and daily life getting in the way of work (P=.05), job intensity (P=.01), a history of burnout (P=.03), anxiety toward the pandemic (P=.04), and environmental noise (P=.01). Destressors found were access to sunlight (P=.02) and fresh air (P<.001) during the workday and going outdoors (P<.001), taking breaks (P<.001), exercising (P<.001), and having social interactions (P<.001). The smartwatch measurements positively related to stress were the number of active intensity periods (P<.001), the number of highly active intensity periods (P=.04), steps (P<.001), and the SD in the heart rate (HR; P<.001). In a multivariate setting, only a history of burnout (P<.001) and family and daily life getting in the way of work (P<.001) were positively associated with stress, while self-reports of social activities (P<.001) and going outdoors (P=.03) were negatively associated with stress. Stress prediction models based on questionnaire data had a similar performance (F1=0.51) compared to models based on automatic measurable data alone (F1=0.47). The results show that there are stressors and destressors when working from home that should be considered when managing stress in employees. Some of these stressors and destressors are (in)directly measurable with unobtrusive sensors, and prediction models based on these data show promising results for the future of automatic stress detection and management. Netherlands Trial Register NL9378; https://trialsearch.who.int/Trial2.aspx?TrialID=NL9378.

Remote patient monitoring at home using ambient sensors: a systematic review

Abstract Background The world population is ageing, and their health needs imply substantial demands on health systems. Remote Patient Monitoring (RPM) may help elderly patients live independently in their homes for longer. The essence of RPM is the continuity of use, which is challenging for wearable devices and patient-led technologies. Unobtrusive (ambient) sensors could be an innovative solution, such as motion detectors and similar technologies. This study aims to review the evidence on the effect of ambient sensors on healthcare use by the elderly. Methods This is a systematic review for narrative synthesis, searching five databases, Medline, Embase, CINHAL, Scopus and Web of Science, on 21 Feb 2022 without setting a lower time limit. No restrictions on the design of studies were applied. A meta-analysis was not feasible due to the heterogenicity of the studies. Results Out of 5,653 search results, 180 studies were subjected to full-text review, of which 6 studies were included in the final synthesis. All the included studies were conducted in the USA. Four studies assessed the technology's cost-efficiency, while only one reported significant cost savings. One study reported a significant reduction in hospital days and visits to a physician among the users. Using ambient sensors was associated with an increased length of stay in facilities where the elderly can live independently, including at home. The impact on the number of hospitalisations or emergency room visits was unclear. Conclusions Our review identified limited evidence on the effect of ambient sensors on healthcare use by the elderly. The potential has been demonstrated for ambient sensor technologies to result in cost savings; however, further research is needed to assess the impact on health outcomes. Key messages

The effect of COVID-19 on the home behaviours of people affected by dementia

The COVID-19 pandemic has dramatically altered the behaviour of most of the world’s population, particularly affecting the elderly, including people living with dementia (PLwD). Here we use remote home monitoring technology deployed into 31 homes of PLwD living in the UK to investigate the effects of COVID-19 on behaviour within the home, including social isolation. The home activity was monitored continuously using unobtrusive sensors for 498 days from 1 December 2019 to 12 April 2021. This period included six distinct pandemic phases with differing public health measures, including three periods of home ‘lockdown’. Linear mixed-effects modelling is used to examine changes in the home activity of PLwD who lived alone or with others. An algorithm is developed to quantify time spent outside the home. Increased home activity is observed from very early in the pandemic, with a significant decrease in the time spent outside produced by the first lockdown. The study demonstrates the effects of COVID-19 lockdown on home behaviours in PLwD and shows how unobtrusive home monitoring can be used to track behaviours relevant to social isolation.

Detecting Burnout of Health Care Professionals in a COVID-19 Testing Laboratory

Health care professionals (HCPs) are frequently exposed to Human Factors/Ergonomics (HFE) issues that result in stress, adversely affecting their health and negatively impacting the quality of care. Chronic stress can result in burnout, with negative implications for individuals, health care organizations, and patients. Current approaches to monitor burnout are reactive and require additional work (e.g., survey completion). In this study, we pilot a methodology using unobtrusive sensors and advanced statistics to bridge this important gap. We collected two types of physiological data - heart rate variability (HRV) and electrodermal activity (EDA) - and measures of perceived workload and burnout from three HCPs in a COVID-19 Testing Laboratory. We identified meaningful relationships between physiological data, workload, and burnout, demonstrating that burnout can be identified proactively using real-time sensor data. Future work will expand the timeframe of data collection and include a larger sample with different types of HCPs.

Estimation of Step Length With Wearable Thigh Sensor Using an Unscented Kalman Filter.

The determination of step length, an important gait parameter, has been a challenging task. Although unobtrusive sensors (inertial measurement units) have been developed recently, they cannot facilitate the automatic estimation of step length. In this article, we use a model-based technique to determine the step length using the Unscented Kalman Filter with angular velocity from a gyroscope inside the thigh pocket. We then propose a novel covariance estimation algorithm based on a screening technique that performs a search for the optimal Process Noise Covariance matrix. Upon implementing the Unscented Kalman Filter, the step length is found using the horizontal position of the foot relative to the hip using a patient-independent robust peak detection algorithm. This research article paves the way for algorithms that are computationally much faster than black box methods, with more scope for the development of better algorithms for covariance estimation using the one proposed in this article as a foundation.

HTIDB: Hierarchical Time-Indexed Database for Efficient Storage and Access to Irregular Time-series Health Sensor Data.

With the enormous amount of data collected by unobtrusive sensors, the potential of utilizing these data and applying various multi-modal advanced analytics on them is numerous and promising. However, taking advantage of the ever-growing data requires high-performance data-handling systems to enable high data scalability and easy data accessibility. This paper demonstrates robust design, developments, and techniques of a hierarchical time-indexed database for decision support systems leveraging irregular and sporadic time series data from sensor systems, e.g., wearables or environmental. We propose a technique that leverages the flexibility of general purpose, high-scalability database systems, while integrating data analytics focused column stores that leverage hierarchical time indexing, compression, and dense raw numeric data storage. We have evaluated the performance characteristics and tradeoffs of each to understand the data access latencies and storage requirements, which are key elements for capacity planning for scalable systems.

Identification of Individually Altered Gait Behavior Using an Unobtrusive IMU Sensor Setup.

Gait behavior is considered an important indicator for the assessment of the general health status and provides a diagnostic observation for neuro-degenerative and musculo-skeletal diseases. Individual changes in gait behavior often reflect a deterioration of the current health status in a general sense and therefore provide significant information for clinicians and care-givers. In this work, we have used an unobtrusive sensor setup comprising three inertial measurement units (IMUs) located at the wrist, the chest and the thigh to obtain an objective measure of the human locomotion. We conducted a clinical trial in a movement laboratory environment to obtain a database of gait data at different walking speeds and conditions. The aging-simulation suit GERT was used to deteriorate the individual gait behavior during the experiments. Treadmill walking trials were used to train different classifiers to discriminate normal walking from GERT-affected walking patterns. Level-ground walking trials were used to validate the previously generated classifiers. A five-fold cross validation during the training process yielded overall F1-scores between 0.965 and 0.986. The validation tests showed promising results with prediction accuracies of more than 80%. Clinical relevance- The clinical relevance of this contri-bution can be considered two-fold. First we demonstrate the possibility of an unobtrusive monitoring system to iden-tify individual deterioration of gait behavior. Second we also validate the use of aging-simulation suits to introduce individual changes of gait patterns in healthy subjects to create a database of simulated yet realistic gait impairments associated with aging.

Using Synthesized IMU Data to Train a Long-Short Term Memory-based Neural Network for Unobtrusive Gait Analysis with a Sparse Sensor Setup.

In this work, we evaluated the possibility to use synthesized IMU data for training a deep neural network to generate a more complex, full-body description of the human gait in terms of joint angle trajectories from a sparse sensor setup. In this context, a sparse sensor setup consists of a few sensors attached to human body segments in an unobtrusive manner to possibly provide a monitoring system in an everyday life scenario. Since the relation between the input IMU data and the output joint angle trajectories is highly non-linear, neural networks appear to provide an optimal framework to formulate a mapping description. Especially with respect to periodic signals, recurrent neural networks (RNNs) have gained importance in the recent years. In this work, we have used a special type of RNNs that can be implemented by using long-short term memory (LSTM) cells, which have shown promising results when being applied to sequential data. The artificial training data was generated by a simulative human gait model and virtually attached sensor devices. The trained network was subsequently validated by a dataset that was recorded from a treadmill walking trial using a motion capturing system and an IMU sensor system. The qualitative comparison already shows promising results, however, this study can only be considered to provide preliminary results in this area. Clinical Relevance- This approach has the potential to be applied in the remote assessment of gait behavior during everyday life environments using an unobtrusive sensor net-work. In particular for monitoring older people suffering from an increased fall risk or any significant gait impairments this work is of possible interest.