Learning from machine learning: prediction of age-related athletic performance decline trajectories

Factors that determine individual age-related decline rates in physical performance are poorly understood and prediction poses a challenge. Linear and quadratic regression models are usually applied, but often show high prediction errors for individual athletes. Machine learning approaches may deliver more accurate predictions and help to identify factors that determine performance decline rates. We hypothesized that it is possible to predict the performance development of a master athlete from a single measurement, that prediction by a machine learning approach is superior to prediction by the average decline curve or an individually shifted decline curve, and that athletes with a higher starting performance show a slower performance decline than those with a lower performance. The machine learning approach was implemented using a multilayer neuronal network. Results showed that performance prediction from a single measurement is possible and that the prediction by a machine learning approach was superior to the other models. The estimated performance decline rate was highest in athletes with a high starting performance and a low starting age, as well as in those with a low starting performance and high starting age, while the lowest decline rate was found for athletes with a high starting performance and a high starting age. Machine learning was superior and predicted trajectories with significantly lower prediction errors compared to conventional approaches. New insights into factors determining decline trajectories were identified by visualization of the model outputs. Machine learning models may be useful in revealing unknown factors that determine the age-related performance decline

Automatic Generation of Labeled Data for Video-Based Human Pose Analysis via NLP applied to YouTube Subtitles

With recent advancements in computer vision as well as machine learning (ML), video-based at-home exercise evaluation systems have become a popular topic of current research. However, performance depends heavily on the amount of available training data. Since labeled datasets specific to exercising are rare, we propose a method that makes use of the abundance of fitness videos available online. Specifically, we utilize the advantage that videos often not only show the exercises, but also provide language as an additional source of information. With push-ups as an example, we show that through the analysis of subtitle data using natural language processing (NLP), it is possible to create a labeled (irrelevant, relevant correct, relevant incorrect) dataset containing relevant information for pose analysis. In particular, we show that irrelevant clips ($n=332$) have significantly different joint visibility values compared to relevant clips ($n=298$). Inspecting cluster centroids also show different poses for the different classes.Comment: 4 pages, 5 figure

Quantifying cardiorespiratory thorax movement with motion capture and deconvolution

Unobtrusive sensing is a growing aspect in the field of biomedical engineering. While many modalities exist, a large fraction of methods ultimately relies on the analysis of thoracic movement. To quantify cardiorespiratory induced thorax movement with spatial resolution, an approach using high-performance motion capture, electrocardiography and deconvolution is presented. In three healthy adults, motion amplitudes are estimated that correspond to values reported in the literature. Moreover, two-dimensional mappings are created that exhibit physiological meaningful relationships. Finally, the analysis of waveform data obtained via deconvolution shows plausible pulse transit behavior

Comparison of Electrode Configurations for Impedance Plethysmography Based Heart Rate Estimation at the Forearm

Electrical impedance plethysmography (EIP) is a cost effective and power efficient physiological measurement method that could potentially be applied for measuring pulse waves along limbs in ambulatory conditions. The pulse wave information could be utilized to determine the heart rate or other relevant parameters such as heart rate variability or cardiac rhythm. We compared three electrode configurations for EIP at the forearm, with the focus on assessing its utility in a wearable device. The evaluation included EIP measurements with ten healthy participants using adhesive gel electrodes. The evaluated electrode configurations were tetrapolar configuration along the forearm and tetrapolar and bipolar configurations around the wrist. For each electrode configuration, the measurements were performed in stationery condition and during finger movement. The collected data was evaluated for finding out differences in the signal to noise ratio (SNR) between the configurations during the two conditions. The results show that pulse wave signal with adequate SNR for heart rate estimation is obtained from the wrist area while stationary and mostly also during the presence of mild movement. There was no significant difference in the data quality between wrist area and conventional configuration along the limb.acceptedVersionPeer reviewe

Accuracy of heart rate variability estimated with reflective wrist-PPG in elderly vascular patients

Optical heart rate monitoring (OHR) with reflective wrist photoplethysmography is a technique mainly used in the wellness application domain for monitoring heart rate levels during exercise. In the absence of motion, OHR technique is also able to estimate individual beat‑to‑beat intervals relatively well and can therefore also be used, for example, in monitoring of cardiac arrhythmias, stress, or sleep quality through heart rate variability (HRV) analysis. HRV analysis has also potential in monitoring the recovery of patients, e.g. after a medical intervention. However, in order to detect subtle changes, the calculated HRV parameters should be sufficiently accurate and very few studies exist that asses the accuracy of OHR derived HRV in non‑healthy subjects. In this paper, we present a method to estimate beat‑to‑beat‑intervals (BBIs) from reflective wrist PPG signal and evaluated the accuracy of the proposed method in estimating BBIs in a cross‑sectional study with 29 hospitalized patients (mean age 70.6 years) in 24‑h recordings performed after peripheral vascular surgery or endovascular interventions. Finally, we evaluate the accuracy of more than 30 commonly used HRV parameters and find that the accuracy of certain metrics, for example SDNN and triangular index, shown in the literature to be associated with the deterioration of the status of the patients during recovery from surgical intervention, could be adequate for patient monitoring. On the other hand, the parameters more affected by the high‑frequency content of the HRV and especially the LF/HF‑ratio should be used with caution

Acceleration of Longitudinal Track and Field Performance Declines in Athletes Who Still Compete at the Age of 100 Years

While physical performance decline rates accelerate after around the age of 70 years, longitudinal athletic performance trends in athletes older than 95 years are unknown. We hypothesized a further accelerated decline in human performance in athletes who still perform at the age of 100 years. To investigate this, longitudinal data of all athletes with results at or over the age of 100 years were collected from the “World Master Rankings” data base spanning 2006–2019 (138 results from 42 athletes; 5 women, 37 men; maximum 105 years) and compared to previously published longitudinal data from 80- to 96-year-old athletes from Sweden (1,134 results from 374 athletes). Regression statistics were used to compare performance decline rates between disciplines and age groups. On average, the individual decline rate of the centenarian group was 2.53 times as steep (100 m: 8.22x; long jump: 0.82x; shot put: 1.61x; discus throw: 1.04x; javelin throw: 0.98x) as that seen in non-centenarians. The steepest increase in decline was found in the 100-m sprint (t-test: p < 0.05, no sign. difference in the other disciplines). The pooled regression statistics of the centenarians are: 100 m: R = 0.57, p = 0.004; long jump: R = 0.90, p < 0.001; shot put: R = 0.65, p < 0.001; discus throw: R = 0.73, p < 0.001; javelin throw: R = 0.68, p < 0.001. This first longitudinal dataset of performance decline rates of athletes who still compete at 100 years and older in five athletics disciplines shows that there is no performance plateau after the age of 90, but rather a further acceleration of the performance decline

Estimating Thoracic Movement with High-Sampling Rate THz Technology

We use a high-sampling rate terahertz (THz) homodyne spectroscopy system to estimate thoracic movement from healthy subjects performing breathing at different frequencies. The THz system provides both the amplitude and phase of the THz wave. From the raw phase information, a motion signal is estimated. An electrocardiogram (ECG) signal is recorded with a polar chest strap to obtain ECG-derived respiration information. While the ECG showed sub-optimal performance for the purpose and only provided usable information for some subjects, the signal derived from the THz system showed good agreement with the measurement protocol. Over all the subjects, a root mean square estimation error of 1.40 BPM is obtained

Design of Hardware Accelerators for Optimized and Quantized Neural Networks to Detect Atrial Fibrillation in Patch ECG Device with RISC-V

Atrial Fibrillation (AF) is one of the most common heart arrhythmias. It is known to cause up to 15% of all strokes. In current times, modern detection systems for arrhythmias, such as single-use patch electrocardiogram (ECG) devices, have to be energy efficient, small, and affordable. In this work, specialized hardware accelerators were developed. First, an artificial neural network (NN) for the detection of AF was optimized. Special attention was paid to the minimum requirements for the inference on a RISC-V-based microcontroller. Hence, a 32-bit floating-point-based NN was analyzed. To reduce the silicon area needed, the NN was quantized to an 8-bit fixed-point datatype (Q7). Based on this datatype, specialized accelerators were developed. Those accelerators included single-instruction multiple-data (SIMD) hardware as well as accelerators for activation functions such as sigmoid and hyperbolic tangents. To accelerate activation functions that require the e-function as part of their computation (e.g., softmax), an e-function accelerator was implemented in the hardware. To compensate for the losses of quantization, the network was expanded and optimized for run-time and memory requirements. The resulting NN has a 7.5% lower run-time in clock cycles (cc) without the accelerators and 2.2 percentage points (pp) lower accuracy compared to a floating-point-based net, while requiring 65% less memory. With the specialized accelerators, the inference run-time was lowered by 87.2% while the F1-Score decreased by 6.1 pp. Implementing the Q7 accelerators instead of the floating-point unit (FPU), the silicon area needed for the microcontroller in 180 nm-technology is below 1 mm²

Video-Based Hand Movement Analysis of Parkinson Patients before and after Medication Using High-Frame-Rate Videos and MediaPipe

Tremor is one of the common symptoms of Parkinson’s disease (PD). Thanks to the recent evolution of digital technologies, monitoring of PD patients’ hand movements employing contactless methods gained momentum. Objective: We aimed to quantitatively assess hand movements in patients suffering from PD using the artificial intelligence (AI)-based hand-tracking technologies of MediaPipe. Method: High-frame-rate videos and accelerometer data were recorded from 11 PD patients, two of whom showed classical Parkinsonian-type tremor. In the OFF-state and 30 Minutes after taking their standard oral medication (ON-state), video recordings were obtained. First, we investigated the frequency and amplitude relationship between the video and accelerometer data. Then, we focused on quantifying the effect of taking standard oral treatments. Results: The data extracted from the video correlated well with the accelerometer-based measurement system. Our video-based approach identified the tremor frequency with a small error rate (mean absolute error 0.229 (±0.174) Hz) and an amplitude with a high correlation. The frequency and amplitude of the hand movement before and after medication in PD patients undergoing medication differ. PD Patients experienced a decrease in the mean value for frequency from 2.012 (±1.385) Hz to 1.526 (±1.007) Hz and in the mean value for amplitude from 8.167 (±15.687) a.u. to 4.033 (±5.671) a.u. Conclusions: Our work achieved an automatic estimation of the movement frequency, including the tremor frequency with a low error rate, and to the best of our knowledge, this is the first paper that presents automated tremor analysis before/after medication in PD, in particular using high-frame-rate video data

Exploring novel algorithms for atrial fibrillation detection by driving graduate level education in medical machine learning

During the lockdown of universities and the COVID-Pandemic most students were restricted to their homes. Novel and instigating teaching methods were required to improve the learning experience and so recent implementations of the annual PhysioNet/Computing in Cardiology (CinC) Challenges posed as a reference. For over 20 years, the challenges have proven repeatedly to be of immense educational value, besides leading to technological advances for specific problems. In this paper, we report results from the class ‘Artificial Intelligence in Medicine Challenge’, which was implemented as an online project seminar at Technical University Darmstadt, Germany, and which was heavily inspired by the PhysioNet/CinC Challenge 2017 ‘AF Classification from a Short Single Lead ECG Recording’. Atrial fibrillation is a common cardiac disease and often remains undetected. Therefore, we selected the two most promising models of the course and give an insight into the Transformer-based DualNet architecture as well as into the CNN-LSTM-based model and finally a detailed analysis for both. In particular, we show the model performance results of our internal scoring process for all submitted models and the near state-of-the-art model performance for the two named models on the official 2017 challenge test set. Several teams were able to achieve F1 scores above/close to 90% on a hidden test-set of Holter recordings. We highlight themes commonly observed among participants, and report the results from the self-assessed student evaluation. Finally, the self-assessment of the students reported a notable increase in machine learning knowledge