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Various factors are responsible for injuries that occur in the U.S. Army soldiers. In particular, rucksack load carriage equipment influences the stability of the lower extremities and possibly affects gait balance. The objective of this investigation was to assess the gait and local dynamic stability of the lower extremity of five subjects as they performed a simulated rucksack march on a treadmill. The Motek Gait Real-time Interactive Laboratory (GRAIL) was utilized to replicate the environment of the rucksack march. The first walking trial was without a rucksack and the second set was executed with the All-Purpose Lightweight Individual Carrying Equipment (ALICE), an older version of the rucksack, and the third set was executed with the newer rucksack version, Modular Lightweight Load Carrying Equipment (MOLLE). In this experiment, the Inertial Measurement Unit (IMU) system, Dynaport was used to measure the ambulatory data of the subject. This experiment required subjects to walk continuously for 200 seconds with a 20kg rucksack, which simulates the real rucksack march training. To determine the dynamic stability of different load carriage and normal walking condition, Local Dynamic Stability (LDS) was calculated to quantify its stability. The results presented that comparing Maximum Lyapunov Exponent (LyE) of normal walking was significantly lower compared to ALICE (P=0.000007) and MOLLE (P=0.00003), however, between ALICE and MOLLE rucksack walking showed no significant difference (P=0.441). The five subjects showed significantly improved dynamic stability when walking without a rucksack in comparison with wearing the equipment. In conclusion, we discovered wearing a rucksack result in a significant (P < 0.0001) reduction in dynamic stability. 

Parkinson’s Disease (PD) is a neurodegenerative disorder affecting the substantia nigra, which leads to more than half of PD patients are considered to be at high risk of falling. Recently, Inertial Measurement Unit (IMU) sensors have shown great promise in the classification of activities of daily living (ADL) such as walking, standing, sitting, and laying down, considered to be normal movement in daily life. Measuring physical activity level from longitudinal ADL monitoring among PD patients could provide insights into their fall mechanisms. In this study, six PD patients (mean age=74.3±6.5 years) and six young healthy subjects (mean age=19.7±2.7 years) were recruited. All the subjects were asked to wear the single accelerometer, DynaPort MM+ (Motion Monitor+, McRoberts BV, The Hague, Netherlands), with a sampling frequency of 100 Hz located at the L5-S1 spinal area for 3 days. Subjects maintained a log of activities they performed and only removed the sensor while showering or performing other aquatic activities. The resultant acceleration was filtered using high and low pass Butterworth filters to determine dynamic and stationary activities. As a result, it was found that healthy young subjects performed significantly more dynamic activities (13.2%) when compared to PD subjects (7%), in contrast, PD subjects (92.9%) had significantly more stationary activities than young healthy subjects (86.8%). 

Acute injury to aged individuals represents a significant challenge to the global healthcare community as these injuries are frequently treated in a reactive method due to the infeasibility of frequent visits to the hospital for biometric monitoring. However, there is potential to prevent a large number of these cases through passive, at-home monitoring of multiple physiological parameters related to various causes that are common to aged adults in general. This research strives to implement wearable devices, ambient “smart home” devices, and minimally invasive blood and urine analysis to test the feasibility of implementation of a multitude of research-level (i.e. not yet clinically validated) methods simultaneously in a “smart system”. The system comprises measures of balance, breathing, heart rate, metabolic rate, joint flexibility, hydration, and physical performance functions in addition to lab testing related to biological aging and mechanical cell strength. A proof-of-concept test is illustrated for two adult males of different ages: a 22-year-old and a 73-year-old matched in body mass index (BMI). The integrated system is test in this work, a pilot study, demonstrating functionality and age-related clinical relevance. The two subjects had physiological measurements taken in several settings during the pilot study: seated, biking, and lying down. Balance measurements indicated changes in sway area of 45.45% and 25.44%, respectively for before/after biking. The 22-year-old and the 73-year-old saw heart rate variabilities of 0.11 and 0.02 seconds at resting conditions, and metabolic rate changes of 277.38% and 222.23%, respectively, in comparison between the biking and seated conditions. A smart camera was used to assess biking speed and the 22- and 73-year-old subjects biked at 60 rpm and 28.5 rpm, respectively. The 22-year-old subject saw a 7 times greater electrical resistance change using a joint flexibility sensor inside of their index finger in comparison with the 73-year-old male. The 22 and 73-year-old males saw respective 28% and 48% increases in their urine ammonium concentration before/after the experiment. The average lengths of the telomere DNA from the two subjects were measured to be 12.1 kb (22-year-old) and 6.9 kb (73-year-old), consistent with their biological ages. The study probed feasibility of 1) multi-metric assessment under free living conditions, and 2) tracking of the various metrics over time.