Objective: Geriatric patients, especially those with dementia or in a delirious state, do not accept conventional contact-based monitoring. Therefore, we propose to measure heart rate (HR) and heart rate variability (HRV) of geriatric patients in a noncontact and unobtrusive way using photoplethysmography imaging (PPGI). Methods: PPGI video sequences were recorded from 10 geriatric patients and 10 healthy elderly people using a monochrome camera operating in the near-infrared spectrum and a colour camera operating in the visible spectrum. PPGI waveforms were extracted from both cameras using superpixel-based regions of interests (ROI). A classifier based on bagged trees was trained to automatically select artefact-free ROIs for HR estimation. HRV was calculated in the time-domain and frequency-domain. Results: an RMSE of 1.03 bpm and a correlation of 0.8 with the reference was achieved using the NIR camera for HR estimation. Using the RGB camera, RMSE and correlation improved to 0.48 bpm and 0.95, respectively. Correlation for HRV in the frequency-domain (LF/HF-ratio) was 0.50 using the NIR camera and 0.70 using the RGB camera. Conclusion: We were able to demonstrate that PPGI is very suitable to measure HR and HRV in geriatric patients. We strongly believe that PPGI will become clinically relevant in monitoring of geriatric patients. Significance: we are the first group to measure both HR and HRV in awake geriatric patients using PPGI. Moreover, we systematically evaluate the effects of the spectrum (near-infrared vs. visible), ROI, and additional motion artefact reduction algorithms on the accuracy of estimated HR and HRV.

Background: The aging population increasingly needs assistive technologies, such as rollators, to function and live less dependently. Rollators are designed to decrease the risk of falls by improving the gait mechanics of their users. However, data on the biomechanics of rollator assisted gait of older adults are limited, or mostly derived from experiments with younger adults. Methods and results: This review summarises the data from 18 independent studies on the kinematic and kinetic gait parameters of assisted gait of older persons. All of these studies evaluated spatio-temporal parameters, but not joint angles or moments. Conclusion: Due to the limited research on rollator supported gait in older adults, the number of parameters that could be analysed in this systematic review was restricted. Further research in the analysis of spatio-temporal parameters and a higher standardisation in clinical research will be necessary.

The detection of muscle contraction and the estimation of muscle force are essential tasks in robot-assisted rehabilitation systems. The most commonly used method to investigate muscle contraction is surface electromyography (EMG), which, however, shows considerable disadvantages in predicting the muscle force, since unpredictable factors may influence the detected force but not necessarily the EMG data. Electrical impedance myography (EIM) investigates the change in electrical impedance during muscle activities and is another promising technique to investigate muscle functions. This paper introduces the design, development, and evaluation of a device that performs EMG and EIM simultaneously for more robust measurement of muscle conditions subject to artifacts. The device is light, wearable, and wireless and has a modular design, in which the EMG, EIM, micro-controller, and communication modules are stacked and interconnected through connectors. As a result, the EIM module measures the bioimpedance between 20 and 200 $\mathsf{\Omega }$ with an error of less than 5% at 140 SPS. The settling time during the calibration phase of this module is less than 1000 ms. The EMG module captures the spectrum of the EMG signal between 20–150 Hz at 1 kSPS with an SNR of 67 dB. The micro-controller and communication module builds an ARM-Cortex M3 micro-controller which reads and transfers the captured data every 1 ms over RF (868 Mhz) with a baud rate of 500 kbps to a receptor connected to a PC. Preliminary measurements on a volunteer during leg extension, walking, and sit-to-stand showed the potential of the system to investigate muscle function by combining simultaneous EMG and EIM.

Obesity and inflammation are reportedly associated with the pathogenesis of sarcopenia, which is characterized by age-related loss of skeletal muscle mass. Intramuscular fat deposits have been found to compromise muscle integrity; however, the relevant fat compounds and their roles as mediators of muscular inflammation are not known. The aim of this study was to identify potential correlations between inflammation markers and lipid compounds that accumulate in the quadriceps muscle of previously described Sprague-Dawley (SD) rat model for high-fat diet- (HFD-) induced muscle loss. Six-month-old SD rats were continuously fed a control (CD) or HFD until the age of 21 months. Magnetic resonance imaging (MRI) revealed a significant decline in muscle cross-sectional area in male SD rats as a result of HFD, but not in female rats. Here, we developed a new procedure to quantitatively identify and classify the fatty acid methyl esters (FAMEs) in rats’ quadriceps muscles from our former study using gas chromatography-mass spectrometry (GC-MS). Fatty acid analysis revealed accumulation of octadecadienoic (linoleic acid), octadecanoic (stearic acid), and octadecenoic (vaccenic acid) acids exclusively in the quadriceps muscles of male rats. The designated fatty acids were mainly incorporated into triacylglycerols (TAGs) or free fatty acids (FFAs), and their proportions were significantly elevated by consumption of a HFD. Furthermore, the number of resident immune cells and the levels of the chemokines RANTES, MCP-1, and MIP-2 were significantly increased in quadriceps muscle tissue of HFD-fed male, but not female rats. Together, HFD-induced muscle loss in aged male SD rats is associated with greater deposits of long-chain fatty acid esters and increased levels of the inflammatory markers RANTES, MCP-1, and MIP-2 in skeletal muscle tissue. This trend is further reinforced by long-term consumption of a HFD, which may provoke synergistic crosstalk between long-chain fatty acids and inflammatory pathways in sarcopenic muscle.

Obesity is a risk factor for chronic kidney disease (CKD). While the exact mechanisms remain unclear, inflammation may be a consequence of obesity that directly impacts the kidneys. The aim of this study was to examine the inflammatory status of the kidneys and potential ongoing renal damage, i.e., tubular damage and fibrosis after long-term obesity maintained through persistent consumption of a high-fat diet (HFD). Twenty-four-week-old male Long-Evans (LEV) rats were continuously fed a control diet (CD) or HFD for 51 weeks. The mean body weight was higher in HFD-fed rats than in control diet-fed rats and markedly elevated during the last 24 weeks. Blood analyses revealed no substantial alterations in renal functional parameters by HFD consumption but a substantial increase in creatine kinase, a muscle loss marker. Magnetic resonance imaging (MRI) was utilized to quantify rat quadriceps muscle mass. The data showed that HFD-induced obesity in LEV rats was accompanied by minor decreases in muscle mass and strength at 75 weeks of age. Rat kidney inflammatory status was evaluated using histological and immunohistological techniques. The number of foci with immune cell infiltrates and infiltrating monocytes/macrophages was significantly increased in HFD-fed rat kidneys at week 75. Renal fibrosis parameters, including glomerulosclerosis and tubular damage, were also markedly increased in renal tissues from HFD-fed rats compared to the controls. The significant increase in tubular protein casts in HFD-fed rat tissues indicated that renal function was already disturbed. Rat kidney inflammatory status was further evaluated using the simultaneous profiling of twenty-two inflammatory markers in kidney tissue extracts. Consistently, MCP-1 and eotaxin (CCL11) levels were elevated in obese LEV rat kidneys. Compared to CD-fed rats, HFD-fed obese LEV rats show significant damage of renal structures with aging. These subtle changes may sensitize the kidneys to the development of progressive CKD.

Stride time variability is an important indicator for the assessment of gait stability. An accurate extraction of the stride intervals is essential for determining stride time variability. Peak detection is a commonly used method for gait segmentation and stride time estimation. Standard peak detection algorithms often fail due to additional movement components and measurement noise. A novel algorithm for robust peak detection in inertial sensor signals was proposed in a previous contribution. In this work, we present a novel approach for estimation of stride time variability based on the formerly proposed peak detection algorithm applied to an unobtrusive sensor setup for motion monitoring. The unobtrusive sensor setup includes a wrist sensor, a pocket or belt sensor, and a necklace sensor, all equipped with both accelerometer and gyroscope. The goal of this work is to implement a generalized approach for accurate and robust stride interval determining algorithm for different sensor locations. Therefore, treadmill and level ground walking experiments were conducted with ten healthy subjects at increasing walking speeds and an age-simulating suit. With the proposed algorithm, we achieved a RMSE of 0.07 s for the stride interval estimation during treadmill walking experiments. The results give promising indications that detection of variation of stride time variability is possible using the proposed unobtrusive sensor setup.