Joint segmentation of multivariate time series with hidden process regression for human activity recognition

The problem of human activity recognition is central for understanding and predicting the human behavior, in particular in a prospective of assistive services to humans, such as health monitoring, well being, security, etc. There is therefore a growing need to build accurate models which can take into account the variability of the human activities over time (dynamic models) rather than static ones which can have some limitations in such a dynamic context. In this paper, the problem of activity recognition is analyzed through the segmentation of the multidimensional time series of the acceleration data measured in the 3-d space using body-worn accelerometers. The proposed model for automatic temporal segmentation is a specific statistical latent process model which assumes that the observed acceleration sequence is governed by sequence of hidden (unobserved) activities. More specifically, the proposed approach is based on a specific multiple regression model incorporating a hidden discrete logistic process which governs the switching from one activity to another over time. The model is learned in an unsupervised context by maximizing the observed-data log-likelihood via a dedicated expectation-maximization (EM) algorithm. We applied it on a real-world automatic human activity recognition problem and its performance was assessed by performing comparisons with alternative approaches, including well-known supervised static classifiers and the standard hidden Markov model (HMM). The obtained results are very encouraging and show that the proposed approach is quite competitive even it works in an entirely unsupervised way and does not requires a feature extraction preprocessing step

Wearables for independent living in older adults: Gait and falls

Solutions are needed to satisfy care demands of older adults to live independently. Wearable technology (wearables) is one approach that offers a viable means for ubiquitous, sustainable and scalable monitoring of the health of older adults in habitual free-living environments. Gait has been presented as a relevant (bio)marker in ageing and pathological studies, with objective assessment achievable by inertial-based wearables. Commercial wearables have struggled to provide accurate analytics and have been limited by non-clinically oriented gait outcomes. Moreover, some research-grade wearables also fail to provide transparent functionality due to limitations in proprietary software. Innovation within this field is often sporadic, with large heterogeneity of wearable types and algorithms for gait outcomes leading to a lack of pragmatic use. This review provides a summary of the recent literature on gait assessment through the use of wearables, focusing on the need for an algorithm fusion approach to measurement, culminating in the ability to better detect and classify falls. A brief presentation of wearables in one pathological group is presented, identifying appropriate work for researchers in other cohorts to utilise. Suggestions for how this domain needs to progress are also summarised

EdgeFall: a promising cloud-edge-end architecture for elderly fall care

Elder citizens face sudden fall, which can lead to injuries of both destructive and non-virulent. These sudden falls are later more precarious than diseases like heart attack, blood sugar, blood pressure because these can be untreated for a lengthy time which can lead to death. Elder citizen who experiences a precipitous fall, carry out their communal life narrowed. Therefore, a shrewd and adequate anti-fallen system is required for aiding elderly health care, specifically to those who live individually. So, it can identify and anticipate a precipitous fall through appropriate human activity recognition. In this study, we have suggested an end-edge-cloud based wearable EdgeFall architecture for elderly care. We have performed simulation setups to clarify the query of why we need such a strategy, and its validity. We have achieved maximum 91.87% accuracy with 1.6% false alarm rate (FAR). These empirical results indicate the superiority of using tightly couple multiple information for recognizing human activity. We can accomplish a low FAR with an enhanced accuracy. We can observe that our proposed end-edge-cloud based architecture can reduce the execution time to millisecond range (ms) of 14.16 to 15.74. This work serves as the starting mark for future related research activities

Could in-home sensors surpass human observation of people with Parkinson’s at high risk of falling? An ethnographic study

Self-report underpins our understanding of falls among people with Parkinson’s (PwP) as they largely happen unwitnessed at home. In this qualitative study, we used an ethnographic approach to investigate which in-home sensors, in which locations, could gather useful data about fall risk. Over six weeks, we observed five independently mobile PwP at high risk of falling, at home. We made field notes about falls (prior events and concerns) and recorded movement with video, Kinect, and wearable sensors. The three women and two men (aged 71 to 79 years) having moderate or severe Parkinson’s were dependent on others and highly sedentary. We most commonly noted balance protection, loss, and restoration during chair transfers, walks across open spaces and through gaps, turns, steps up and down, and tasks in standing (all evident walking between chair and stairs, e.g.). Our unobtrusive sensors were acceptable to participants: they could detect instability during everyday activity at home and potentially guide intervention. Monitoring the route between chair and stairs is likely to give information without invading the privacy of people at high risk of falling, with very limited mobility, who spend most of the day in their sitting rooms

Is the timed-up and go test feasible in mobile devices? A systematic review

The number of older adults is increasing worldwide, and it is expected that by 2050 over 2 billion individuals will be more than 60 years old. Older adults are exposed to numerous pathological problems such as Parkinson’s disease, amyotrophic lateral sclerosis, post-stroke, and orthopedic disturbances. Several physiotherapy methods that involve measurement of movements, such as the Timed-Up and Go test, can be done to support efficient and effective evaluation of pathological symptoms and promotion of health and well-being. In this systematic review, the authors aim to determine how the inertial sensors embedded in mobile devices are employed for the measurement of the different parameters involved in the Timed-Up and Go test. The main contribution of this paper consists of the identification of the different studies that utilize the sensors available in mobile devices for the measurement of the results of the Timed-Up and Go test. The results show that mobile devices embedded motion sensors can be used for these types of studies and the most commonly used sensors are the magnetometer, accelerometer, and gyroscope available in off-the-shelf smartphones. The features analyzed in this paper are categorized as quantitative, quantitative + statistic, dynamic balance, gait properties, state transitions, and raw statistics. These features utilize the accelerometer and gyroscope sensors and facilitate recognition of daily activities, accidents such as falling, some diseases, as well as the measurement of the subject's performance during the test execution.info:eu-repo/semantics/publishedVersio

System (for) Tracking Equilibrium and Determining Incline (STEADI)

The goal of this project was to design and implement a smartphone-based wearable system to detect fall events in real time. It has the acronym STEADI. Rather than have expensive customised hardware STEADI was implemented in a cost effective manner using a generic mobile computing device. In order to detect the fall event, we propose a fall detector that uses the accelerometer available in a mobile phone. As for detecting a fall we mainly divide the system in two sections, the signal processing and classification. For the processing both a median filter and a high pass filter are used. A Median filter is used to amplify/enhance the signal by removing impulsive noise while preserving the signal shape while the High pass filter is used to emphasise transitions in the signal. Then, in order to recognize a fall event, our STEADI system implements two methods that are a simple threshold analysis to determine whether or not a fall has occurred (threshold-based) and a more sophisticated Naïve-Bayes classification method to differentiate falling from other mobile activities. Our experimental results show that by applying the signal processing and Naïve-Bayes classification together increases the accuracy by more than 20% compared with using the threshold-based method alone. The Naïve-Bayes achieved a detection accuracy of 95% in overall. Furthermore, an external sensor is introduced in order to enhance its accuracy. In addition to the fall detection, the systems can also provide location information using Google Maps as to the whereabouts of the fall event using the available GPS on the smartphone and sends the message to the caretaker via an SMS

System (for) Tracking Equilibrium and Determining Incline (STEADI)

The goal of this project was to design and implement a smartphone-based wearable system to detect fall events in real time. It has the acronym STEADI. Rather than have expensive customised hardware STEADI was implemented in a cost effective manner using a generic mobile computing device. In order to detect the fall event, we propose a fall detector that uses the accelerometer available in a mobile phone. As for detecting a fall we mainly divide the system in two sections, the signal processing and classification. For the processing both a median filter and a high pass filter are used. A Median filter is used to amplify/enhance the signal by removing impulsive noise while preserving the signal shape while the High pass filter is used to emphasise transitions in the signal. Then, in order to recognize a fall event, our STEADI system implements two methods that are a simple threshold analysis to determine whether or not a fall has occurred (threshold-based) and a more sophisticated Naïve-Bayes classification method to differentiate falling from other mobile activities. Our experimental results show that by applying the signal processing and Naïve-Bayes classification together increases the accuracy by more than 20% compared with using the threshold-based method alone. The Naïve-Bayes achieved a detection accuracy of 95% in overall. Furthermore, an external sensor is introduced in order to enhance its accuracy. In addition to the fall detection, the systems can also provide location information using Google Maps as to the whereabouts of the fall event using the available GPS on the smartphone and sends the message to the caretaker via an SMS

Design and Validation of a Minimal Complexity Algorithm for Stair Step Counting

Wearable sensors play a significant role for monitoring the functional ability of the elderly and in general, promoting active ageing. One of the relevant variables to be tracked is the number of stair steps (single stair steps) performed daily, which is more challenging than counting flight of stairs and detecting stair climbing. In this study, we proposed a minimal complexity algorithm composed of a hierarchical classifier and a linear model to estimate the number of stair steps performed during everyday activities. The algorithm was calibrated on accelerometer and barometer recordings measured using a sensor platform worn at the wrist from 20 healthy subjects. It was then tested on 10 older people, specifically enrolled for the study. The algorithm was then compared with other three state-of-the-art methods, which used the accelerometer, the barometer or both. The experiments showed the good performance of our algorithm (stair step counting error: 13.8%), comparable with the best state-of-the-art (p > 0.05), but using a lower computational load and model complexity. Finally, the algorithm was successfully implemented in a low-power smartwatch prototype with a memory footprint of about 4 kB

Improving activity recognition using a wearable barometric pressure sensor in mobility-impaired stroke patients.

© 2015 Massé et al.Background: Stroke survivors often suffer from mobility deficits. Current clinical evaluation methods, including questionnaires and motor function tests, cannot provide an objective measure of the patients mobility in daily life. Physical activity performance in daily-life can be assessed using unobtrusive monitoring, for example with a single sensor module fixed on the trunk. Existing approaches based on inertial sensors have limited performance, particularly in detecting transitions between different activities and postures, due to the inherent inter-patient variability of kinematic patterns. To overcome these limitations, one possibility is to use additional information from a barometric pressure (BP) sensor. Methods: Our study aims at integrating BP and inertial sensor data into an activity classifier in order to improve the activity (sitting, standing, walking, lying) recognition and the corresponding body elevation (during climbing stairs or when taking an elevator). Taking into account the trunk elevation changes during postural transitions (sit-to-stand, stand-to-sit), we devised an event-driven activity classifier based on fuzzy-logic. Data were acquired from 12 stroke patients with impaired mobility, using a trunk-worn inertial and BP sensor. Events, including walking and lying periods and potential postural transitions, were first extracted. These events were then fed into a double-stage hierarchical Fuzzy Inference System (H-FIS). The first stage processed the events to infer activities and the second stage improved activity recognition by applying behavioral constraints. Finally, the body elevation was estimated using a pattern-enhancing algorithm applied on BP. The patients were videotaped for reference. The performance of the algorithm was estimated using the Correct Classification Rate (CCR) and F-score. The BP-based classification approach was benchmarked against a previously-published fuzzy-logic classifier (FIS-IMU) and a conventional epoch-based classifier (EPOCH). Results: The algorithm performance for posture/activity detection, in terms of CCR was 90.4 %, with 3.3 % and 5.6 % improvements against FIS-IMU and EPOCH, respectively. The proposed classifier essentially benefits from a better recognition of standing activity (70.3 % versus 61.5 % [FIS-IMU] and 42.5 % [EPOCH]) with 98.2 % CCR for body elevation estimation. Conclusion: The monitoring and recognition of daily activities in mobility-impaired stoke patients can be significantly improved using a trunk-fixed sensor that integrates BP, inertial sensors, and an event-based activity classifier