Gait analysis is an important medical diagnostic process and has many applications in healthcare, rehabilitation, therapy, and exercise training. However, typical gait analysis has to be performed in ...a gait laboratory, which is inaccessible for a large population and cannot provide natural gait measures. In this paper, we present a novel sensor device, namely, Smart Insole, to tackle the challenge of efficient gait monitoring in real life. An array of electronic textile (eTextile)-based pressure sensors are integrated in the insole to fully measure the plantar pressure. Smart Insole is also equipped with a low-cost inertial measurement unit including a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer to capture the gait characteristics in motion. Smart Insole can offer precise acquisition of gait information. Meanwhile, it is lightweight, thin, and comfortable to wear, providing an unobtrusive way to perform the gait monitoring. Furthermore, a smartphone graphic user interface is developed to display the sensor data in real-time via Bluetooth low energy. We perform a set of experiments in four real-life scenes including hallway walking, ascending/descending stairs, and slope walking, where gait parameters and features are extracted. Finally, the limitation and improvement, wearability and usability, further work, and healthcare-related potential applications are discussed.
Quality of sleep is an important indicator of health and well being. Recent developments in the field of in-home sleep monitoring have the potential to enhance a person's sleeping experience and ...contribute to an overall sense of well being. Existing in-home sleep monitoring devices either fail to provide adequate sleep information or are obtrusive to use. To overcome these obstacles, a noncontact and cost-effective sleep monitoring system, named SleepSense, is proposed for continuous recognition of the sleep status, including on-bed movement, bed exit, and breathing section. SleepSense consists of three parts: a Doppler radar-based sensor, a robust automated radar demodulation module, and a sleep status recognition framework. Herein, several time-domain and frequency-domain features are extracted for the sleep recognition framework. A prototype of SleepSense is presented and evaluated using two sets of experiments. In the short-term controlled experiment, the SleepSense achieves an overall 95.1% accuracy rate in identifying various sleep status. In the 75-minute sleep study, SleepSense demonstrates wide usability in real life. The error rate for breathing rate extraction in this study is only 6.65%. These experimental results indicate that SleepSense is an effective and promising solution for in-home sleep monitoring.
Nurses regularly perform patient handling activities. These activities with awkward postures expose healthcare providers to a high risk of overexertion injury. The recognition of patient handling ...activities is the first step to reduce injury risk for caregivers. The current practice on workplace activity recognition is based on human observational approach, which is neither accurate nor projectable to a large population. In this paper, we aim at addressing these challenges. Our solution comprises a smart wearable device and a novel spatio-temporal warping (STW) pattern recognition framework. The wearable device, named Smart Insole 2.0, is equipped with a rich set of sensors and can provide an unobtrusive way to automatically capture the information of patient handling activities. The STW pattern recognition framework fully exploits the spatial and temporal characteristics of plantar pressure by calculating a novel warped spatio-temporal distance, to quantify the similarity for the purpose of activity recognition. To validate the effectiveness of our framework, we perform a pilot study with eight subjects, including eight common activities in a nursing room. The experimental results show the overall classification accuracy achieves 91.7%. Meanwhile, the qualitative profile and load level can also be classified with accuracies of 98.3% and 92.5%, respectively.
The past decades have witnessed a rapid surge in new sensing and monitoring devices for well-being and healthcare. One key representative in this field is body sensor networks (BSNs). However, with ...advances in sensing technologies and embedded systems, wireless communication has gradually become one of the dominant energy-consuming sectors in BSN applications. Recently, compressed sensing (CS) has attracted increasing attention in solving this problem due to its enabled sub-Nyquest sampling rate. In this paper, we investigate the quantization effect in CS architecture and argue that the quantization configuration is a critical factor of the energy efficiency for the entire CS architecture. To this end, we present a novel configurable quantized compressed sensing (QCS) architecture, in which the sampling rate and quantization are jointly explored for better energy efficiency. Furthermore, to combat the computational complexity of the configuration procedure, we propose a rapid configuration algorithm, called RapQCS. According to the experiments involving several categories of real biosignals, the proposed configurable QCS architecture can gain more than 66% performance-energy tradeoff than the fixed QCS architecture. Moreover, our proposed RapQCS algorithm can achieve over 150× speedup on average, while decreasing the reconstructed signal fidelity by only 2.32%.
Compressed sensing (CS) is an emerging sampling paradigm in data acquisition. Its integrated analog-to-information structure can perform simultaneous data sensing and compression with low-complexity ...hardware. To date, most of the existing CS implementations have a fixed architectural setup, which lacks flexibility and adaptivity for efficient dynamic data sensing. In this paper, we propose a dynamic knob (DK) design to effectively reconfigure the CS architecture by recognizing the biosignals. Specifically, the dynamic knob design is a template-based structure that comprises a supervised learning module and a look-up table module. We model the DK performance in a closed analytic form and optimize the design via a dynamic programming formulation. We present the design on a 130 nm process, with a 0.058 mm 2 fingerprint and a 187.88 nJ/event energy-consumption. Furthermore, we benchmark the design performance using a publicly available dataset. Given the energy constraint in wireless sensing, the adaptive CS architecture can consistently improve the signal reconstruction quality by more than 70%, compared with the traditional CS. The experimental results indicate that the ultra-low power dynamic knob can provide an effective adaptivity and improve the signal quality in compressed sensing towards biosignal dynamics.
Sleep posture is a key component in sleep quality assessment and pressure ulcer prevention. Currently, body pressure analysis has been a popular method for sleep posture recognition. In this paper, a ...matching-based approach, Body-Earth Mover's Distance (BEMD), for sleep posture recognition is proposed. BEMD treats pressure images as weighted 2D shapes, and combines EMD and Euclidean distance for similarity measure. Compared with existing work, sleep posture recognition is achieved with posture similarity rather than multiple features for specific postures. A pilot study is performed with 14 persons for six different postures. The experimental results show that the proposed BEMD can achieve 91.21% accuracy, which outperforms the previous method with an improvement of 8.01%.
Energy in wireless communication is the dominant sector of the energy consumption in electroencephalography (EEG) telemonitoring due to intrinsically high throughput. Analog-to-information ...conversion, i.e., compressed sensing (CS), offers a promising solution to attack this problem. Most of previous research work on CS focus on the sparse representation to reduce the signal dimension, but the impact of quantization in CS has had limited examination in the research community. In this brief, we investigate the quantization effects of CS with the application in EEG telemonitoring. In particular, we study the quantized CS (QCS) structure to explore the impacts of quantization on the performance-energy (P-E) tradeoff of the front end in EEG telemonitoring. Compared to the state-of-the-art CS with the constant bit resolution, experiments show that the QCS framework with the optimal bit resolution can improve the P-E tradeoff by more than 35%. Furthermore, the optimal bit strategy even broadens the application range of the QCS framework by 54% compared to the traditional Nyquist sampling, which indicates that the quantization is a critical factor in the entire CS framework.
Wearable computing systems have spurred many opportunities to continuously monitor human bodies with sensors worn on or implanted in the body. These emerging platforms have started to revolutionize ...many fields, including healthcare and wellness applications, particularly when integrated with intelligent analytic capabilities. However, a significant challenge that computer architects are facing is how to embed sophisticated analytic capabilities in wearable computers in an energy-efficient way while not compromising system performance. In this paper, we present XPro, a novel cross-end analytic engine architecture for wearable computing systems. The proposed cross-end architecture is able to realize a generic classification design across wearable sensors and a data aggregator with high energy-efficiency. To facilitate the practical use of XPro, we also develop an Automatic XPro Generator that formally generates XPro instances according to specific design constraints. As a proof of concept, we study the design and implementation of XPro with six different health applications. Evaluation results show that, compared with state-of-the-art methods, XPro can increase the battery life of the sensor node by 1.6-2.4X while at the same time reducing system delay by 15.6-60.8% for wearable computing systems.
Wireless sensor nodes advance the brain-computer interface (BCI) from laboratory setup to practical applications. Compressed sensing (CS) theory provides a sub-Nyquist sampling paradigm to improve ...the energy efficiency of electroencephalography (EEG) signal acquisition. However, EEG is a structure-variational signal with time-varying sparsity, which decreases the efficiency of compressed sensing. In this paper, we present a new adaptive CS architecture to tackle the challenge of EEG signal acquisition. Specifically, we design a dynamic knob framework to respond to EEG signal dynamics, and then formulate its design optimization into a dynamic programming problem. We verify our proposed adaptive CS architecture on a publicly available data set. Experimental results show that our adaptive CS can improve signal reconstruction quality by more than 70% under different energy budgets while only consuming 187.88 nJ/event. This indicates that the adaptive CS architecture can effectively adapt to the EEG signal dynamics in the BCI.
Patient handling activities with awkward postures expose healthcare providers to a high risk of overexertion injury. The recognition of patient handling activities (PHA) is the first step to reduce ...injury risk for caregivers. In this paper, we propose a system to solve the problem, which comprises an unobtrusive wearable device and a novel spatio-temporal warping (STW) pattern recognition framework. The wearable device, named Smart Insole 2.0, is equipped with a rich set of sensors and can capture the information of patient handling activities. The STW pattern recognition framework fully exploits the spatial and temporal characteristics of plantar pressure, to quantify the similarity for the purpose of activity recognition. we perform a pilot study with eight subjects, including eight common activities in a nursing room. The experimental results show the overall classification accuracy achieves 91.7%. Meanwhile, the qualitative profile and load level can also be classified with accuracies of 98.3% and 92.5%, respectively.
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