Although numerous studies have analyzed the relationship between manual material handling (MMH) and the forces acting on the lumbar spine, the difference in the MMH between experts and novices through the analysis of measured data has not been well studied. The purpose of this paper was to analyze the difference in the MMH positions between ten skilled experts working at a freight transport company (Group 1) and five unskilled novices without any experience (Group 2) during asymmetric lifting. All the human subjects performed asymmetric lifting experiments with closed eyes; the experiments involved moving loads (6 and 18 kg) to the left side. Time series data of the vertical ground reaction force were measured, using a Wii Balance Board, and then, the center-of-pressure (CoP) trajectories were calculated. The balance board was used for the measurement, because it was reliable, inexpensive, and portable and provided good repeatability even on rough surfaces, and all the information pertaining to the load and worker under various conditions was captured without any omissions. Under the 18 kg load condition, the CoP positions for Group 2 were located on the same side during left asymmetric lifting; however, those for Group 1 were located on the opposite side during left asymmetric lifting ($P <;0.001$ ). Furthermore, under the 6 kg load condition, the load weight influenced asymmetric lifting for most subjects of Group 2 such that the CoP positions were located on the opposite side ($P <;0.001$ ). Based on the simulation and electromyography measurement results, we inferred that the difference in the CoP positions between the two different groups could be attributed to the difference in the hip positions. Most skilled experts position their hips in such a way that their CoP trajectories move toward the opposite side during left asymmetric lifting. Although the skillful characteristics of experts may be responsible for the lightening of the burden on the waist during asymmetric lifting, there are still two points that this paper does not clarify: the relationship between the experts' adjustment of the hip position and the load of the weight, and the influence of an imbalance of the CoP position on the forces acting on the lumbar spine.

Numerous studies have developed self-reliance support robots, such as those assisting the sit-to-stand (STS) movement, which requires coordination between the upper body and the lower limbs. However, few studies have quantitatively evaluated the service quality of such robots. This paper proposes a method to evaluate the service quality of STS-assistance robots through the relative phase (RP), which contains information on the coordinating relationship between the upper body and the lower limbs. STS experiments were performed under three conditions, namely unassisted STS movement and robot-supported STS movements lasting 2 and 5 s. The results showed that the quality of robot assistance during STS movement could be quantitatively evaluated through RP. Furthermore, three features—minimum RP, mean absolute RP, and deviation phase (DP)—that contained information on the users response to the robot could be extracted for data mining. Moreover, electromyography performed to verify the experimental results confirmed the relationship between coordinated performance and muscle activities during STS movements. Thus, evaluating STS movements through the RP is an effective method of evaluating the service quality of robots, and features extracted from RP theory could distinguish classes of movements with a high probability.

Many studies have reported that the human body-balance ability was essential in the early detection and self-management of chronic diseases. However, devices to measure balance, such as motion capture and force plates, are expensive and require a particular space for installation as well as specialized knowledge for analysis. Therefore, this study aimed to propose and verify a new algorithm to score the human body-balance ability on the wobble board (HBBAWB), based on a geometric solution using a cheap and portable device. Although the center of gravity (COG), the projected point of the center of mass (COM) on the fixed ground, has been used as the index for the balance ability, generally, it was not proper to use the COG under the condition of no fixed environment. The reason was that the COG index did not include the information on the slope for the wobble. Thus, this study defined the new index as the perpendicular-projection point (PPP), which was the projected point of the COM on the tilted plane. The proposed geometric solution utilized the relationship among three points, the PPP, the COM, and the middle point between the two feet, via linear regression. The experimental results found that the geometric solution, which utilized the relationship between the three angles of the equivalent model, enabled us to score the HBBAWB.

Senior citizens have increased plasma glucose and a higher risk of diabetes-related complications than young people. However, it is difficult to diagnose and manage elderly diabetics because there is no clear symptom according to current diagnostic criteria. They also dislike the invasive blood sample test. This study aimed to classify a difference in gait and physical fitness characteristics between senior citizens with and without diabetes for a non-invasive method and propose a machine-learning-based personal home-training system for training abnormal gait motions by oneself. We used a dataset for classification with 200 over 65-year-old elders who walked a flat and straight 15 m route in 3 different walking speed conditions using an inertial measurement unit and physical fitness test. Then, questionnaires for participants were included to identify life patterns. Through results, it was found that there were abnormalities in gait and physical fitness characteristics related to balance ability and walking speed. Using a single RGB camera, the developed training system for improving abnormalities enabled us to correct the exercise posture and speed in real-time. It was discussed that there are risks and errors in the training system based on human pose estimation for future works.