BACKGROUND: The current approach to assessing bradykinesia in Parkinson\'s Disease relies on the Unified Parkinson\'s Disease Rating Scale (UPDRS), which is a numeric scale. Inertial sensors offer the ability to probe subcomponents of bradykinesia: motor speed, amplitude, and rhythm. Thus, we sought to investigate the differential effects of high-frequency compared to low-frequency subthalamic nucleus (STN) deep brain stimulation (DBS) on these quantified facets of bradykinesia.
METHODS: We recruited advanced Parkinson\'s Disease subjects with a chronic bilateral subthalamic nucleus (STN) DBS implantation to a single-blind stimulation trial where each combination of medication state (OFF/ON), electrode contacts, and stimulation frequency (60 Hz/180 Hz) was assessed. The Kinesia One sensor system was used to measure upper limb bradykinesia. For each stimulation trial, subjects performed extremity motor tasks. Sensor data were recorded continuously. We identified STN DBS parameters that were associated with improved upper extremity bradykinesia symptoms using a mixed linear regression model.
RESULTS: We recruited 22 subjects (6 females) for this study. The 180 Hz STN DBS (compared to the 60 Hz STN DBS) and dopaminergic medications improved all subcomponents of upper extremity bradykinesia (motor speed, amplitude, and rhythm). For the motor rhythm subcomponent of bradykinesia, ventral contacts yielded improved symptom improvement compared to dorsal contacts.
CONCLUSIONS: The differential impact of high- and low-frequency STN DBS on the symptoms of bradykinesia may advise programming for these patients but warrants further investigation. Wearable sensors represent a valuable addition to the armamentarium that furthers our ability to conduct objective, quantitative clinical assessments.

In recent years, advancements in the Internet of Things (IoT), manufacturing processes, and material synthesis technologies have positioned flexible sensors as critical components in wearable devices. These developments are propelling wearable technologies based on flexible sensors towards higher intelligence, convenience, superior performance, and biocompatibility. Recently, two-dimensional nanomaterials known as MXenes have garnered extensive attention due to their excellent mechanical properties, outstanding electrical conductivity, large specific surface area, and abundant surface functional groups. These notable attributes confer significant potential on MXenes for applications in strain sensing, pressure measurement, gas detection, etc. Furthermore, polymer substrates such as polydimethylsiloxane (PDMS), polyurethane (PU), and thermoplastic polyurethane (TPU) are extensively utilized as support materials for MXene and its composites due to their light weight, flexibility, and ease of processing, thereby enhancing the overall performance and wearability of the sensors. This paper reviews the latest advancements in MXene and its composites within the domains of strain sensors, pressure sensors, and gas sensors. We present numerous recent case studies of MXene composite material-based wearable sensors and discuss the optimization of materials and structures for MXene composite material-based wearable sensors, offering strategies and methods to enhance the development of MXene composite material-based wearable sensors. Finally, we summarize the current progress of MXene wearable sensors and project future trends and analyses.

Plastics accumulating in the environment are nowadays of great concern for aquatic systems and for the living organisms populating them. In this context, nanoplastics (NPs) are considered the major and most dangerous contaminants because of their small size and active surface, which allow them to interact with a variety of other molecules. Current methods used for the detection of NPs rely on bulky and expensive techniques such as spectroscopy. Here we propose, for the first time, a novel, fast, and easy-to-use sensor based on an electrolyte-gated field-effect transistor (EG-FET) with a carbon nanotube (CNT) semiconductor (EG-CNTFET) for the detection of NPs in aquatic environments, using polystyrene NPs (PS-NPs) as a model material. In particular, as a working mechanism for the EG-CNTFETs we exploited the ability of CNTs and PS to form noncovalent interactions. Indeed, in our EG-CNTFET devices, the interaction between NPs and CNTs caused a change in the electric double layers. A linear increase in the corrected on current (*ION) of the EG-CNTFETs, with a sensitivity of 9.68 μA/(1 mg/mL) and a linear range of detection from 0.025 to 0.25 mg/mL were observed. A π-π interaction was hypothesized to take place between the two materials, as indicated by an X-ray photoelectron spectroscopy analysis. Using artificial seawater as an electrolyte, to mimic a real-case scenario, a linear increase in *ION was also observed, with a sensitivity of 6.19 μA/(1 mg/mL), proving the possibility to use the developed sensor in more complex solutions, as well as in low concentrations. This study offers a starting point for future exploitation of electrochemical sensors for NP detection and identification.

The synchronization phenomenon in nature has been utilized in sensing and timekeeping fields due to its numerous advantages, including amplitude and frequency stabilization, noise reduction, and sensitivity improvement. However, the limited synchronization bandwidth hinders its broader application, and few techniques have been explored to enhance this aspect. In this paper, we conducted theoretical and experimental studies on the unidirectional synchronization characteristics of a resonator with phase lock loop oscillation. A novel enhancement method for the synchronization bandwidth using a parametrically excited MEMS oscillator is proposed, which achieves a remarkably large synchronization bandwidth of 8.85 kHz, covering more than 94% of the hysteresis interval. Importantly, the proposed method exhibits significant potential for high-order synchronization and frequency stabilization compared to the conventional directly excited oscillator. These findings present an effective approach for expanding the synchronization bandwidth, which has promising applications in nonlinear sensing, fully mechanical frequency dividers, and high-precision time references.

With the rapid development of modern science and technology, navigation technology provides great convenience for people\'s life, but the problem of inaccurate localization in complex environments has always been a challenge that navigation technology needs to be solved urgently. To address this challenge, this paper proposes an augmented reality navigation method that combines image segmentation and multi-sensor fusion tracking registration. The method optimizes the image processing process through the GA-OTSU-Canny algorithm and combines high-precision multi-sensor information in order to achieve accurate tracking of positioning and guidance in complex environments. Experimental results show that the GA-OTSU-Canny algorithm has a faster image edge segmentation rate, and the fastest start speed is only 1.8 s, and the fastest intersection selection time is 1.2 s. The navigation system combining the image segmentation and sensor tracking and registration techniques has a highly efficient performance in real-world navigation, and its building recognition rates are all above 99%. The augmented reality navigation system not only improves the navigation accuracy in high-rise and urban canyon environments, but also significantly outperforms traditional navigation solutions in terms of navigation startup time and target building recognition accuracy. In summary, this research not only provides a new framework for the theoretical integration of image processing and multi-sensor data, but also brings innovative technical solutions for the development and application of practical navigation systems.

BACKGROUND: Older adults are at greater risk of eating rotten fruits and of getting food poisoning because cognitive function declines as they age, making it difficult to distinguish rotten fruits. To address this problem, researchers have developed and evaluated various tools to detect rotten food items in various ways. Nevertheless, little is known about how to create an app to detect rotten food items to support older adults at a risk of health problems from eating rotten food items.
OBJECTIVE: This study aimed to (1) create a smartphone app that enables older adults to take a picture of food items with a camera and classifies the fruit as rotten or not rotten for older adults and (2) evaluate the usability of the app and the perceptions of older adults about the app.
METHODS: We developed a smartphone app that supports older adults in determining whether the 3 fruits selected for this study (apple, banana, and orange) were fresh enough to eat. We used several residual deep networks to check whether the fruit photos collected were of fresh fruit. We recruited healthy older adults aged over 65 years (n=15, 57.7%, males and n=11, 42.3%, females) as participants. We evaluated the usability of the app and the participants\' perceptions about the app through surveys and interviews. We analyzed the survey responses, including an after-scenario questionnaire, as evaluation indicators of the usability of the app and collected qualitative data from the interviewees for in-depth analysis of the survey responses.
RESULTS: The participants were satisfied with using an app to determine whether a fruit is fresh by taking a picture of the fruit but are reluctant to use the paid version of the app. The survey results revealed that the participants tended to use the app efficiently to take pictures of fruits and determine their freshness. The qualitative data analysis on app usability and participants\' perceptions about the app revealed that they found the app simple and easy to use, they had no difficulty taking pictures, and they found the app interface visually satisfactory.
CONCLUSIONS: This study suggests the possibility of developing an app that supports older adults in identifying rotten food items effectively and efficiently. Future work to make the app distinguish the freshness of various food items other than the 3 fruits selected still remains.

Current methods for detecting pipeline oil leaks depend primarily on optical detection, which can be slow and have deployment limitations. An alternative non-optical approach for earlier and faster detection of oil leaks would enable a rapid response and reduce the environmental impact of oil leaks. Here, we demonstrate that organic electrochemical transistors (OECTs) can be used as non-optical sensors for crude oil detection in subsea environments. OECTs are thin film electronic devices that can be used for sensing in a variety of environments, but they have not yet been tested for crude oil detection in subsea environments. We fabricated OECTs with poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) as the channel and showed that coating the channel with a polystyrene film results in an OECT with a large and measurable response to oil. Oil that comes in contact with the device will adsorb onto the polystyrene film and increases the impedance at the electrolyte interface. We performed electrochemical impedance spectroscopy measurements to quantify the impedance across the device and found an optimal thickness for the polystyrene coating for the detection of oil. Under optimal device characteristics, as little as 10 μg of oil adsorbed on the channel surface produced a statistically significant change in the source-drain current. The OECTs were operable in seawater for the detection of oil, and we demonstrated that the devices can be transferred to flexible substrates which can be easily implemented in vehicles, pipelines, or other surfaces. This work demonstrates a low-cost device for oil detection in subsea environments and provides a new application of OECT sensors for sensing.

Graphene, a 2D nanomaterial, has garnered significant attention in recent years due to its exceptional properties, offering immense potential for revolutionizing various technological applications. In the context of the Internet of Things (IoT), which demands seamless connectivity and efficient data processing, graphene\'s unique attributes have positioned it as a promising candidate to prevail over challenges and optimize IoT systems. This review paper aims to provide a brief sketch of the diverse applications of graphene in IoT, highlighting its contributions to sensors, communication systems, and energy storage devices. Additionally, it discusses potential challenges and prospects for the integration of graphene in the rapidly evolving IoT landscape.

Modifying the coordination or local environments of single-, di-, tri-, and multi-metal atom (SMA/DMA/TMA/MMA)-based materials is one of the best strategies for increasing the catalytic activities, selectivity, and long-term durability of these materials. Advanced sheet materials supported by metal atom-based materials have become a critical topic in the fields of renewable energy conversion systems, storage devices, sensors, and biomedicine owing to the maximum atom utilization efficiency, precisely located metal centers, specific electron configurations, unique reactivity, and precise chemical tunability. Several sheet materials offer excellent support for metal atom-based materials and are attractive for applications in energy, sensors, and medical research, such as in oxygen reduction, oxygen production, hydrogen generation, fuel production, selective chemical detection, and enzymatic reactions. The strong metal-metal and metal-carbon with metal-heteroatom (i.e., N, S, P, B, and O) bonds stabilize and optimize the electronic structures of the metal atoms due to strong interfacial interactions, yielding excellent catalytic activities. These materials provide excellent models for understanding the fundamental problems with multistep chemical reactions. This review summarizes the substrate structure-activity relationship of metal atom-based materials with different active sites based on experimental and theoretical data. Additionally, the new synthesis procedures, physicochemical characterizations, and energy and biomedical applications are discussed. Finally, the remaining challenges in developing efficient SMA/DMA/TMA/MMA-based materials are presented.

OBJECTIVE: Mid-flexion instability has been identified as a cause for dissatisfaction following total knee arthroplasty (TKA). Robotic-assisted surgery using the Mako robot only allows for assessment of stability at 10° and 90°. This study aimed to investigate any evidence of mid-flexion instability in Mako-assisted TKA.
METHODS: Data from 72 TKA in 59 patients from 2018 to 2022 were collected. All patients underwent an RA (Mako, Stryker, Fort Lauderdale, FL, USA), single-radius design, cruciate-retaining TKA. Intraoperatively, medial, and lateral pressures were measured at 10°, 45° and 90° of flexion using a pressure sensor (Verasense, OrthoSensor, 59 Inc., Dania Beach, FL, USA). The knee was considered balanced if the difference in pressures between compartments was less than 15 pounds-force (lbf).
RESULTS: There was no significant difference between the pressures measured in the medial compartment at 10°, 45° and 90° of flexion (P = 0.696). A statistically significant difference was found between the pressures measured in the lateral compartment at 10°, 45° and 90° of flexion, with the 10° value being significantly higher (P < 0.001), but this did not exceed the threshold of 15 lbf. None of the patients had a pressure difference of more than 15 lbf when pressures at 45° were compared to that at 10° and 90°, medially or laterally.
CONCLUSIONS: This study showed no evidence of mid-flexion instability in Mako-assisted TKA, using a single radius, cruciate-retaining prosthesis whilst maintaining the joint height.
METHODS: Level III retrospective cohort study.