Fully Automatic Camera for Personalized Highlight Generation in Sporting Events.

Personally curated content in short-form video formats provides added value for participants and spectators but is often disregarded in lower-level events because it is too labor-intensive to create or is not recorded at all. Our smart sensor-driven tripod focuses on supplying a unified sensor and video solution to capture personalized highlights for participants in various sporting events with low computational and hardware costs. The relevant parts of the video for each participant are automatically determined by using the timestamps of his/her received sensor data. This is achieved through a customizable clipping mechanism that processes and optimizes both video and sensor data. The clipping mechanism is driven by sensing nearby signals of Adaptive Network Topology (ANT+) capable devices worn by the athletes that provide both locality information and identification. The device was deployed and tested in an amateur-level cycling race in which it provided clips with a detection rate of 92.9%. The associated sensor data were used to automatically extract peloton passages and report riders' positions on the course, as well as which participants were grouped together. Insights derived from sensor signals can be processed and published in real time, and an upload optimization scheme is proposed that can provide video clips for each rider a maximum of 5 min after the passage if video upload is enabled.

A smartphone-based solution to monitor daily physical activity in a care home.

INTRODUCTION: In an ageing population, increasing chronic disease prevalence puts a high economic burden on society. Physical activity plays an important role in disease prevention and should therefore be promoted in the elderly. METHODS: In this study, a mobile health (mHealth) system was implemented in a care home setting to monitor and promote elderly peoples' daily activity. The physical activity of 20 elderly people (8 female and 12 male, aged 81 ± 9 years old) was monitored over 10 weeks using the mHealth system, consisting of a smartphone and heart rate belt. Feedback on physical activity was provided weekly. A reference performance test battery derived from the Senior Fitness Test determined the participants' physical fitness. RESULTS: Activity levels increased from week 1 onwards, peaking at week 5, and decreasing slightly until week 10. This illustrates that the use of mHealth and feedback on physical activity can motivate the elderly to become more active, but that the effect is transient without other incentives. Bio-data from the mHealth system were translated into a fitness score explaining 65% of the test battery's variance. After separating the elderly into three groups depending on physical fitness determined from the test battery, classification based on the fitness score resulted in a correct classification rate of 67.3%. DISCUSSION: This study demonstrates that an mHealth system can be implemented in a care home setting to motivate activity of the elderly, and that the bio-data can be translated in a fitness score predicting the outcome of labour-intensive tests.

Estimation of muscle fatigue using surface electromyography and near-infrared spectroscopy.

This study looks at various parameters, derived from surface electromyography (sEMG) and Near Infrared Spectroscopy (NIRS) and their relationship in muscle fatigue during a static elbow flexion until exhaustion as well as during a semidynamic exercise.We found a linear increasing trend for a corrected amplitude parameter and a linear decreasing slope for the frequency content of the sEMG signal. The tissue oxygenation index (TOI) extracted from NIRS recordings showed a four-phase response for all the subjects. A strong correlation between frequency content of the sEMG signal and TOI was established. We can conclude that both sEMG and NIRS give complementary information concerning muscle fatigue.

Sigh rate and respiratory variability during mental load and sustained attention.

Spontaneous breathing consists of substantial correlated variability: Parameters characterizing a breath are correlated with parameters characterizing previous and future breaths. On the basis of dynamic system theory, negative emotion states are predicted to reduce correlated variability whereas sustained attention is expected to reduce total respiratory variability. Both are predicted to evoke sighing. To test this, respiratory variability and sighing were assessed during a baseline, stressful mental arithmetic task, nonstressful sustained attention task, and recovery in between tasks. For respiration rate (excluding sighs), reduced total variability was found during the attention task, whereas correlated variation was reduced during mental load. Sigh rate increased during mental load and during recovery from the attention task. It is concluded that mental load and task-related attention show specific patterns in respiratory variability and sigh rate.

Instantaneous changes in heart rate regulation due to mental load in simulated office work.

The cardiac regulation effects of a mental task added to regular office work are described. More insight into the time evolution during the different tasks is created by using time-frequency analysis (TFA). Continuous wavelet transformation was applied to create time series of instantaneous power and frequency in specified frequency bands (LF 0.04-0.15 Hz; HF 0.15-0.4 Hz), in addition to the traditional linear heart rate variability (HRV) parameters. In a laboratory environment, 43 subjects underwent a protocol with three active conditions: a clicking task with low mental load and a clicking task with high mental load (mental arithmetic) performed twice, each followed by a rest condition. The heart rate and measures related to vagal modulation could differentiate the active conditions from the rest condition, meaning that HRV is sensitive to any change in mental or physical state. Differences between physical and mental stress were observed and a higher load in the combined task was observed. Mental stress decreased HF power and caused a shift toward a higher instantaneous frequency in the HF band. TFA revealed habituation to the mental load within the task (after 3 min) and between the two tasks with mental load. In conclusion, the use of TFA in this type of analysis is important as it reveals extra information. The addition of a mental load to a physical task elicited further effect on HRV parameters related to autonomic cardiac modulation.

Time-frequency heart rate variability characteristics of young adults during physical, mental and combined stress in laboratory environment.

The goal of this study was to evaluate the changes in heart rate variability (HRV) parameters due to a specific physical, mental or combined load. More specifically, the difference in effect between mental load and physical activity is studied. In addition, the effect of the combined physical and mental demand on the HRV parameters was examined and compared with the changes during the single task. In a laboratory environment, 28 subjects went through a protocol with different types of load (physical and/or mental), each followed by a period of rest. Continuous wavelet transformation was applied to create time series of instantaneous power and frequency in specified frequency bands (LF and HF). HF could distinguish the active conditions from the rest condition, meaning that HRV is sensitive to any change in mental or physical state. Differences in HRV parameters were observed between physical, mental and the combined load. In conclusion, we were able to distinguish between rest, physical and mental condition by combining different HRV characteristics. The addition of a mental load to a physical task had an extra effect on the HRV characteristics.

Source separation from single-channel recordings by combining empirical-mode decomposition and independent component analysis.

In biomedical signal processing, it is often the case that many sources are mixed into the measured signal. The goal is usually to analyze one or several of them separately. In the case of multichannel measurements, several blind source separation techniques are available for decomposing the signal into its components [e.g., independent component analysis (ICA)]. However, only a few techniques have been reported for analyses of single-channel recordings. Examples are single-channel ICA (SCICA) and wavelet-ICA (WICA), which all have certain limitations. In this paper, we propose a new method for a single-channel signal decomposition. This method combines empirical-mode decomposition with ICA. We compare the separation performance of our algorithm with SCICA and WICA through simulations, and we show that our method outperforms the other two, especially for high noise-to-signal ratios. The performance of the new algorithm was also demonstrated in two real-life applications.

Take a deep breath: the relief effect of spontaneous and instructed sighs.

Spontaneous sighing is related to subjective relief of negative emotional states. Whether this also applies to instructed sighing is not known. The present study aimed to investigate sEMG and respiratory variability (1) during recovery from mental stress with and without an instructed sigh; (2) before and after spontaneous sighs throughout the experiment. A spontaneous sigh was preceded by increasing sEMG and increasing random respiratory variability, and followed by decreasing sEMG and increased structured correlated respiratory variability. Following an instructed sigh, a smaller reduction in sEMG and an increase in random respiratory variability during recovery from mental stress were observed. Thus, a spontaneous sigh seemed to induce relief. An instructed sigh appeared to inhibit recovery from mental stress.

Wavelet-independent component analysis to remove electrocardiography contamination in surface electromyography.

Removing artifacts from biomedical signals, such as surface electromyography (sEMG), has become a major research topic in biomedical signal processing. In electromyography signals, a source of contamination is the electrophysiological signal of the heart (ECG signals). This contamination influences features extracted from the sEMG, especially during low-activity measurements of the muscles such as during mental stress. As the heart is a muscle, the frequency content of the heart signals overlaps the frequency content of the muscle signals, so basic frequency filtering is not possible. In this paper, we present the results of a recently developed algorithm: wavelet-independent component analysis. We compare these results with the widely described algorithm of ECG template subtraction for removing ECG contamination.

Textile integrated contactless EMG sensing for stress analysis.

Stress has become an important issue in today's society. Forty to fifty percent of the work related illnesses are directly or indirectly related to stress. In the ConText project, a biofeedback shirt for daily use is being developed to register muscle activity. The user receives feedback about muscle fatigue and/or the level of stress in order to lower the risk on musculoskeletal disorders. Comfort is an important factor to lower the acceptance threshold to wear the shirt. To achieve optimal comfort, the project aims for unobtrusive measurements with contactless sensors which are textile integrated. Working with contactless sensors induces new challenges for instance the displacement of the sensor in the shirt relative to the anatomical position of the muscles. This could affect the recorded signal and lead to errors in the signal. In this paper, we present the results of the quantification of this misalignment. Secondly, we present the first tests with the embroidered sensor.