Proper cane handling is one of the main goals of cane therapy in the field of physical rehabilitation. Toward that goal, a system has been developed to monitor the weight-bearing performance of the patient and provide an audio feedback to affect a patient's weight application onto the cane. The smart cane is designed to read the force measurement, to compare the read-out force and to generate an alert message if the force applied by the patient is incorrect. The system includes a graphical user interface, which allows real-time graphing of the force measurements and keeps a database for offline comparative data analysis by the therapist. It employs a wireless connection between the microcontroller and the computer using Bluetooth technology, which increases mobility of the patient. This will help the patient to achieve optimum rehabilitation that can lead to an improved balance in walking and eventually cane independence.

In this paper, we present an Internet of things (IoT)-based data collection system for the risk assessment of urinary stone formation, or urolithiasis, by the measurement and storage of four parameters in urine: pH, concentrations of ionized calcium (Ca²⁺), uric acid and total dissolved solids. The measurements collected by the system from patients and healthy individuals grouped by age and gender will be stored in a cloud database. These will be used in the training phase of an artificial intelligence (AI) machine learning process utilizing the logistics regression model. The trained model provides a binary risk assessment, indicating if the end user is either a stone-former or not. For system validation, standard chemical solutions were used. Preliminary results indicated a sufficient measurement range, falling within the physiological range, and resolution for pH (2.0–10.0, +/−0.1), Ca²⁺(0.1–3.0 mmol/l, +/−0.05), uric acid (20–500 ppm, +/−1) and conductivity (1.0–40.0 mS/cm, +/−0.1), exhibiting high correlation with standard instruments. We intend to deploy this system in few hospitals in Taiwan to collect the data of patients’ urine, with analysis aided by urologist assessments for the risk of urolithiasis. The modularized design allows future modification and expansion to accommodate other sensing analytes.

Wearable medical devices (WMDs) for healthcare applications have become ubiquitous, allowing remote, at-home, and real-time chronic monitoring that have significantly decongested clinics. These WMDs permitted the monitoring of several physiological parameters, such as heart and respiration rates, SPO₂, temperature, and energy expenditure during activities of daily living (ADLs) or fitness activities. While the measurement of these parameters has become common, full noninvasive, unobtrusive, and real-time blood pressure (BP) monitoring remains elusive owing to BP’s complex dynamics. To bring this into fruition, several works have been conducted combining different biosignals to indirectly extract BP by using PTT. Unlike previous works, we considered PTT variability by averaging it over discrete durations to account for BP variability for a more accurate estimation. PTTs were obtained using electrocardiograph (ECG) and reflective photoplethysmograph (rPPG) signals extracted by a wearable device attached to a single site on the upper arm. Our results show a significant correlation between average PTT and the BP measured using auscultation in a trial study. The developed system has potential for chronic, noninvasive, and cuff-less blood pressure monitors (BPMs) for localized and single-site implementations. Meanwhile, real-time data from the wearable device may be accessed via a remote desktop or a mobile phone application.

This work presents the design and fabrication of a wearable microfluidic patch-based system for sweat collection with a calorimetric flow rate sensor based on heat convection for measuring sweat rate (SR). The effects were predicted using a 3D multi-physics simulator and were verified on a fabricated patch made of polyimide layers. The sensor can detect surface temperature gradients of 302–312 K caused by fluid flowing thru the microfluidic channels at a rate of 0.5–23 µg s⁻¹ that fall within the physiological range of SR. Meanwhile, the relation between flow rate and temperature gradient is highly linear (Pearson r² = 0.999) and repeatable. This work also demonstrates a low-cost method for patterning microfluidic channels on flexible substrates which can be used for mass production of wearable patches.

This work provides an overview of the present state-of-the-art in the development of deep brain Deep Brain Stimulation (DBS) and how such devices alleviate motor and cognitive disorders for a successful aging. This work reviews chronic diseases that are addressable via DBS, reporting also the treatment efficacies. The underlying mechanism for DBS is also reported. A discussion on hardware developments focusing on DBS control paradigms is included specifically the open- and closed-loop “smart” control implementations. Furthermore, developments towards a “smart” DBS, while considering the design challenges, current state of the art, and constraints, are also presented. This work also showcased different methods, using ambient energy scavenging, that offer alternative solutions to prolong the battery life of the DBS device. These are geared towards a low maintenance, semi-autonomous, and less disruptive device to be used by the elderly patient suffering from motor and cognitive disorders.