Electric wheelchairs make it easier for disabled and elderly people to live, move, interact, and participate in society. Moving a wheelchair in open spaces is relatively easy, but in closed and small spaces, maneuvering is difficult. Solutions to such problems for people with disabilities are applicable to a relatively small group of recipients and are mostly custom-made solutions, whose considerable cost is a significant barrier to accessibility. New technologies can provide an opportunity to improve the quality of life of people with disabilities in this aspect. Using selected elements of complex automation and control systems, cost-effective solutions can be created that facilitate the functioning of people with disabilities. This paper presents an analysis of hazards and problems when maneuvering a wheelchair in narrow passageways, as well as the authors’ solution to this problem, and the concept and assumptions of a mechatronic anti-collision system based on 2D LiDAR laser scanners. This solution is composed of a proprietary 2D rotating scanner mechanism that ensures the acquisition of 3D images of the environment around the wheelchair. Preliminary tests of this solution yielded promising results. Further research will include miniaturization of the device.

The purpose of this study was to examine the accuracy of HR measurements by the PTN-104 sensor in comparison to the fingertip pulse oximeter, which is a photoplethysmographic sensor (PPG). Twelve healthy participants underwent the same protocol during a single visit. Measurements were taken after each participant completed an initial rest period of 5 minutes and after 1-minute of exercising comprising of 30 squats. Each subject had the PTN-104 sensor attached to the index finger and a fingertip pulse oximeter to the opposite one. When examining the data in aggregate, there was a strong correlation between the PTN-104 sensor and PPG for HR (r = 0.988) with a mean bias of −2.55 bpm (95 % LoA +5.0, −10.1). The PTN-104 sensor satisfied validity criteria for HR monitors, however, showed a lower accuracy for measurements at rest, which is surprising. Due to the noticed limitations, this study should be repeated with a larger group of subjects and the PTN-104 sensor should be compared to the gold standard method for measuring HR, which is ECG.

Electric wheelchairs significantly enhance mobility for individuals with disabilities, but navigating confined or crowded spaces remains a challenge. This paper presents a mechatronic anticollision system based on Time-of-Flight (ToF) sensors designed to improve wheelchair navigation in such environments. The system performs eight-plane 3D environmental scans in 214–358 ms, with a vertical field of view of 12.4° and a detection range of up to 4 m—sufficient for effective obstacle avoidance. Unlike existing solutions like the YDLIDAR T-mini Plus, which has a narrow vertical field of view and a longer detection range that may be excessive for indoor spaces, or the xLIDAR, which struggles with shorter detection ranges, our system balances an optimal detection range and vertical scanning area, making it especially suitable for wheelchair users. Preliminary tests confirm that our system achieves high accuracy, with a standard deviation as low as 0.003 m and a maximum deviation below 0.05 m at a 3-m range on high-reflectivity surfaces (e.g., white and light brown). Our solution offers low power consumption (140 mA) and USB communication, making it an energy-efficient and easy-to-integrate solution for electric wheelchairs. Future work will focus on enhancing angular precision and robustness for dynamic, real-world environments.